Wireless Sensor Network (WSNs) are deployed at aggressive environments which are vulnerable to various security attacks such as Wormholes, Denial of Attacks and Sybil Attacks. There are various intrusion detection techniques that are used to identify attacks in a network with high accuracy level. This paper has focused on Denial of Service attack, since it is the most common attack that affects the environment severely. Therefore a new hybrid technique combining Hidden Markov Model with Ant Colony Optimization (HMM+ACO) has been
proposed that gives improved performance than the other techniques.
IRJET- An Efficient Model for Detecting and Identifying Cyber Attacks in Wire...
This document proposes an efficient model for detecting and identifying cyber attacks in wireless networks using deep learning approaches. The model is designed to perform feature selection and classification on network data to detect malicious behavior. The model architecture includes input, hidden, and output layers for feature extraction, and uses a random forest classifier trained on the NSL KDD Cup dataset. Experimental results using the KDD Cup and NSL-KDD datasets show the model can accurately classify network behaviors and detect cyber attacks with over 82% accuracy.
With the colossal growth of wireless sensor networks (WSNs) in different applications starting from home
automation to military affairs, the pressure on ensuring security in such a network is paramount.
Considering the security challenges, it is really a hard-hitting effort to develop a secured WSN system.
Moreover, as the information technology is getting popular, the intruders are also planning new ideas to
break the system security, to harm the network and to make the system quality down with the target of
taking the control of the network to corrupt it or to get benefits from it anyway. The intruders corrupt the
system only when the security breaking cost (SBC) is lower compared with the benefits they attained or the
harm it can make to others. In this paper, the authors define the term “maximizing network interruption
problem” and propose a technique, called the grid point approximation algorithm, to estimate the SBC of a
multi-hop WSN so that it can be made tougher for an intruder to break the system security. It is assumed
that the intruder has the complete picture of the entire network. The technique is designed from the
intruder’s point of view for completely jamming all the sensor nodes in the network through placing
jammers or malicious nodes strategically and at the same time keeping the number of jammer nodes to
minimum or near minimum. To the best of the authors’ knowledge, there is no work proposed so far of the
same kind. Experimental results with the changes of the different network parameters show that the
proposed algorithm is able to provide excellent performances to achieve the targets.
A survey of Network Intrusion Detection using soft computing Technique
with the impending era of internet, the network security has become the key foundation for lot of financial and business application. Intrusion detection is one of the looms to resolve the problem of network security. An Intrusion Detection System (IDS) is a program that analyses what happens or has happened during an execution and tries to find indications that the computer has been misused. Here we propose a new approach by utilizing neuro fuzzy and support vector machine with fuzzy genetic algorithm for higher rate of detection.
Finding Critical Link and Critical Node Vulnerability for Network
The document discusses network vulnerability assessment and finding critical links and nodes. It proposes using a belief propagation algorithm to calculate the vulnerability of each node and the overall network vulnerability over time. It provides an example network and shows the results of analyzing it to find the critical nodes and links using the proposed algorithm. The algorithm works by having each node calculate the vulnerability of its neighbors and share this information over time to determine the overall network vulnerability.
Preemptive modelling towards classifying vulnerability of DDoS attack in SDN ...
Software-Defined Networking (SDN) has become an essential networking concept towards escalating the networking capabilities that are highly demanded future internet system, which is immensely distributed in nature. Owing to the novel concept in the field of network, it is still shrouded with security problems. It is also found that the Distributed Denial-of-Service (DDoS) attack is one of the prominent problems in the SDN environment. After reviewing existing research solutions towards resisting DDoS attack in SDN, it is found that still there are many open-end issues. Therefore, these issues are identified and are addressed in this paper in the form of a preemptive model of security. Different from existing approaches, this model is capable of identifying any malicious activity that leads to a DDoS attack by performing a correct classification of attack strategy using a machine learning approach. The paper also discusses the applicability of best classifiers using machine learning that is effective against DDoS attack.
DESIGN AND EFFICIENT DEPLOYMENT OF HONEYPOT AND DYNAMIC RULE BASED LIVE NETWO...
The continuously emerging, operationally and managerially independent, geographically distributed computer networks deployable in an evolutionarily manner have created greater challenges in securing them. Several research works and experiments have convinced the security expert that Network Intrusion Detection Systems (NIDS) or Network Intrusion Prevention Systems (NIPS) alone are not capable of securing the Computer Networks from internal and external threats completely. In this paper we present the design of Intrusion Collaborative System which is a combination of NIDS,NIPS, Honeypots, software tools like nmap, iptables etc. Our Design is tested against existing attacks based on Snort Rules and several customized DDOS , remote and guest attacks. Dynamic rules are generated during every unusual behavior that helps Intrusion Collaborative System to continuously learn about new attacks. Also a formal approach to deploy Live Intrusion Collaboration Systems based on System of Systems Concept is Proposed.
A Survey on Data Intrusion schemes used in MANETIRJET Journal
The document discusses data intrusion schemes used in mobile ad hoc networks (MANETs). It reviews common problems with data intrusion in MANETs due to their dynamic architecture and limited resources. Several proposed intrusion detection schemes are described, including distributed and cooperative schemes, specification-based schemes, and the proposed Random Walker Detection method. The proposed method aims to efficiently detect intrusions by deploying detection engines at each node and excluding detection engines from random walkers to reduce detection latency. It is described as working on three network layers and using advanced encryption standards to securely detect and route around malicious nodes.
This document proposes applying convolutional neural networks for network intrusion detection. It presents the methodology of using CNN and hybrid CNN-RNN models on two network intrusion detection datasets, KDDCup '99 and NSL-KDD. The results show CNN and hybrid models performed better than multi-layer perceptrons at classifying connections as normal or attacks and categorizing different attack types. Future work would evaluate these models on more recent intrusion detection datasets.
Secure intrusion detection and attack measure selectionUvaraj Shan
This document proposes NICE, a framework for secure intrusion detection and attack mitigation in virtual network systems. NICE uses distributed agents on cloud servers to monitor traffic, detect vulnerabilities, and generate attack graphs. It profiles virtual machines to identify their state and vulnerabilities. When potential attacks are detected, NICE can quarantine suspicious VMs and inspect their traffic. The attack analyzer correlates alerts, constructs attack graphs, and selects appropriate countermeasures based on the graphs. Evaluations show NICE can effectively detect attacks while minimizing performance overhead for the cloud system.
IRJET- An Efficient Model for Detecting and Identifying Cyber Attacks in Wire...IRJET Journal
This document proposes an efficient model for detecting and identifying cyber attacks in wireless networks using deep learning approaches. The model is designed to perform feature selection and classification on network data to detect malicious behavior. The model architecture includes input, hidden, and output layers for feature extraction, and uses a random forest classifier trained on the NSL KDD Cup dataset. Experimental results using the KDD Cup and NSL-KDD datasets show the model can accurately classify network behaviors and detect cyber attacks with over 82% accuracy.
Maximizing network interruption in wirelessIJCNCJournal
With the colossal growth of wireless sensor networks (WSNs) in different applications starting from home
automation to military affairs, the pressure on ensuring security in such a network is paramount.
Considering the security challenges, it is really a hard-hitting effort to develop a secured WSN system.
Moreover, as the information technology is getting popular, the intruders are also planning new ideas to
break the system security, to harm the network and to make the system quality down with the target of
taking the control of the network to corrupt it or to get benefits from it anyway. The intruders corrupt the
system only when the security breaking cost (SBC) is lower compared with the benefits they attained or the
harm it can make to others. In this paper, the authors define the term “maximizing network interruption
problem” and propose a technique, called the grid point approximation algorithm, to estimate the SBC of a
multi-hop WSN so that it can be made tougher for an intruder to break the system security. It is assumed
that the intruder has the complete picture of the entire network. The technique is designed from the
intruder’s point of view for completely jamming all the sensor nodes in the network through placing
jammers or malicious nodes strategically and at the same time keeping the number of jammer nodes to
minimum or near minimum. To the best of the authors’ knowledge, there is no work proposed so far of the
same kind. Experimental results with the changes of the different network parameters show that the
proposed algorithm is able to provide excellent performances to achieve the targets.
A survey of Network Intrusion Detection using soft computing Techniqueijsrd.com
with the impending era of internet, the network security has become the key foundation for lot of financial and business application. Intrusion detection is one of the looms to resolve the problem of network security. An Intrusion Detection System (IDS) is a program that analyses what happens or has happened during an execution and tries to find indications that the computer has been misused. Here we propose a new approach by utilizing neuro fuzzy and support vector machine with fuzzy genetic algorithm for higher rate of detection.
Finding Critical Link and Critical Node Vulnerability for Networkijircee
The document discusses network vulnerability assessment and finding critical links and nodes. It proposes using a belief propagation algorithm to calculate the vulnerability of each node and the overall network vulnerability over time. It provides an example network and shows the results of analyzing it to find the critical nodes and links using the proposed algorithm. The algorithm works by having each node calculate the vulnerability of its neighbors and share this information over time to determine the overall network vulnerability.
Preemptive modelling towards classifying vulnerability of DDoS attack in SDN ...IJECEIAES
Software-Defined Networking (SDN) has become an essential networking concept towards escalating the networking capabilities that are highly demanded future internet system, which is immensely distributed in nature. Owing to the novel concept in the field of network, it is still shrouded with security problems. It is also found that the Distributed Denial-of-Service (DDoS) attack is one of the prominent problems in the SDN environment. After reviewing existing research solutions towards resisting DDoS attack in SDN, it is found that still there are many open-end issues. Therefore, these issues are identified and are addressed in this paper in the form of a preemptive model of security. Different from existing approaches, this model is capable of identifying any malicious activity that leads to a DDoS attack by performing a correct classification of attack strategy using a machine learning approach. The paper also discusses the applicability of best classifiers using machine learning that is effective against DDoS attack.
DESIGN AND EFFICIENT DEPLOYMENT OF HONEYPOT AND DYNAMIC RULE BASED LIVE NETWO...IJNSA Journal
The continuously emerging, operationally and managerially independent, geographically distributed computer networks deployable in an evolutionarily manner have created greater challenges in securing them. Several research works and experiments have convinced the security expert that Network Intrusion Detection Systems (NIDS) or Network Intrusion Prevention Systems (NIPS) alone are not capable of securing the Computer Networks from internal and external threats completely. In this paper we present the design of Intrusion Collaborative System which is a combination of NIDS,NIPS, Honeypots, software tools like nmap, iptables etc. Our Design is tested against existing attacks based on Snort Rules and several customized DDOS , remote and guest attacks. Dynamic rules are generated during every unusual behavior that helps Intrusion Collaborative System to continuously learn about new attacks. Also a formal approach to deploy Live Intrusion Collaboration Systems based on System of Systems Concept is Proposed.
CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SETIJNSA Journal
In network security framework, intrusion detection is one of a benchmark part and is a fundamental way to protect PC from many threads. The huge issue in intrusion detection is presented as a huge number of false alerts; this issue motivates several experts to discover the solution for minifying false alerts according to data mining that is a consideration as analysis procedure utilized in a large data e.g. KDD CUP 99. This paper presented various data mining classification for handling false alerts in intrusion detection as reviewed. According to the result of testing many procedure of data mining on KDD CUP 99 that is no individual procedure can reveal all attack class, with high accuracy and without false alerts. The best accuracy in Multilayer Perceptron is 92%; however, the best Training Time in Rule based model is 4 seconds . It is concluded that ,various procedures should be utilized to handle several of network attacks.
Energy efficient ccrvc scheme for secure communications in mobile ad hoc netw...eSAT Publishing House
This document summarizes a research paper that proposes an energy efficient certificate revocation scheme (EECCRVC) for secure communications in mobile ad hoc networks. The scheme aims to both revoke intruder certificates to exclude them from the network and utilize node energy effectively. It adopts a certificate revocation scheme (CCRVC) that deals with false accusations while outperforming other techniques in revoking intruder certificates. The scheme also enhances reliability and accuracy by promptly vindicating warned nodes based on a threshold mechanism. Experimental results using the NS-2 simulator show that the proposed EECCRVC scheme provides secure communications with effective energy utilization in mobile ad hoc networks.
IRJET- Improving Cyber Security using Artificial IntelligenceIRJET Journal
This document discusses using artificial intelligence techniques like machine learning algorithms to improve cyber security. It proposes a methodology that uses Splunk to extract relevant fields from cybersecurity data, feeds that into a K-means clustering algorithm to form attack clusters, then sends those clusters to individual artificial neural networks (ANNs). The aggregated ANN results are then fed into a support vector machine (SVM) which classifies attacks as malicious, non-malicious, or benign. Testing this approach on a dataset achieved a classification accuracy of over 92% when using Splunk, K-means, ANNs, and SVM together.
A Top-down Hierarchical Multi-hop Secure Routing Protocol for Wireless Sensor...ijasuc
This paper proposes a new top-down hierarchical, multi-hop, secure routing protocol for the wireless
sensor network, which is resilient to report fabrication attack. The report fabrication attack tries to
generate bogus reports by compromising the sensor nodes to mislead the environment monitoring
application executed by randomly deployed wireless sensor nodes. The proposed protocol relies on
symmetric key mechanism which is appropriate for random deployment of wireless sensor nodes. In the
proposed protocol, base station initiates the synthesis of secure hierarchical topology using top down
approach. The enquiry phase of the protocol provides assurance for the participation of all the cluster
heads in secure hierarchical topology formation. Further, this methodology takes care of failure of head
node or member node of a cluster. This protocol ensures confidentiality, integrity, and authenticity of the
final report of the monitoring application. The simulation results demonstrate the scalability of the
proposed protocol.
NOVEL HYBRID INTRUSION DETECTION SYSTEM FOR CLUSTERED WIRELESS SENSOR NETWORKIJNSA Journal
Wireless sensor network (WSN) is regularly deployed in unattended and hostile environments. The WSN is vulnerable to security threats and susceptible to physical capture. Thus, it is necessary to use effective mechanisms to protect the network. It is widely known, that the intrusion detection is one of the most efficient security mechanisms to protect the network against malicious attacks or unauthorized access. In this paper, we propose a hybrid intrusion detection system for clustered WSN. Our intrusion framework uses a combination between the Anomaly Detection based on support vector machine (SVM) and the Misuse Detection. Experiments results show that most of routing attacks can be detected with low false alarm.
This document summarizes a research paper that proposes a rule-based technique using fuzzy logic to detect security attacks in wireless sensor networks. The paper identifies 10 common security attacks in wireless sensor networks including denial of service, eavesdropping, traffic analysis, etc. A fuzzy rule-based system is developed to calculate the impact of these security attacks. The system uses MATLAB tools and mouse dataset to test performance. Case studies are presented to demonstrate how the system can predict the likelihood and impact of security attacks on a wireless sensor network.
M.Rizwan Khalid is seeking a position to utilize his IT skills and contribute to organizational growth as a responsible team member. He has a BS(Hons) in IT from University of Education Lahore, Multan Campus with a GPA of 75%. His skills include programming languages such as HTML, CSS, ASP.net, C#, C++, Java, SQL, and JavaScript. He has experience with projects in C#, Oracle, and databases.
Este documento describe las grutas de Belén donde se cree que Jesús nació y fue visitado por los pastores. Describe las diferentes cavidades de la gruta que se consideran lugares sagrados como donde Jesús fue concebido, nació, y José y María pasaron tiempo con él. Explica cómo Dios vive incluso en los lugares más humildes y cómo los pastores encontraron y adoraron a Dios en la pobreza del pesebre.
Este documento trata sobre los sistemas operativos y el hardware de las computadoras. Explica las funciones básicas de un sistema operativo como la interfaz de usuario, administración de recursos, archivos y tareas. También describe diferentes tipos de sistemas operativos como monotarea, multitarea, monousuario y multiusuario. Finalmente, define el hardware como los componentes físicos de una computadora e incluye periféricos de entrada, salida y memoria RAM.
This document provides examples of how to format bibliographic citations for different sources, including books, encyclopedia articles, websites, and database articles. It lists the author's name, title, publisher, date of publication, and access date as the key pieces of information to include for each type of source.
Review of Security Issues in Mobile Wireless Sensor NetworksEswar Publications
MWSNs are finding applicability in wide range of applications. Applications spread from day to day utilities to military and surveillance, where they may sense information about vehicular movements around border. Considering the importance of data being sent by these nodes, threat of compromising them has also increased. This paper aims to explore various types of attacks and tries to classify them based on some common parameter. Better understanding of various attacks, their style of functioning and point of penetration can help researchers devise better preventive measures.
Kell blood group system most important blood group system following to ABO and Rh blood group system, particularly RhD as far as immunogenicity is concerned and Its clinical importance.
The Internet of Things is one of the single biggest disruptive factors in today’s digital landscape. Companies need to plan out an IoT strategy that allows them to use data to create personalized content for customers across different channels.
Boris Kraft, Chief Visionary Officer of Magnolia, will be explaining the role of the digital business platform, and how it should form the hub for a company’s web, mobile and Internet of Things initiatives.
IRJET- Implementation of Artificial Intelligence Methods to Curb Cyber Assaul...IRJET Journal
This document discusses how artificial intelligence methods can help curb cyber assaults. It reviews various AI techniques including expert systems, artificial neural networks, and intelligent agents that have been implemented or could potentially be implemented for cyber security purposes. For example, expert systems have been used to analyze risk levels on e-commerce sites and identify system vulnerabilities. Artificial neural networks have been applied for intrusion detection and classification of attacks. Intelligent agents are well-suited for combating cyber crimes due to their mobility, flexibility, and cooperative nature. The document concludes that while AI is already being used in various ways for cyber security, hackers may also start using AI techniques, presenting new challenges going forward.
Secure intrusion detection and attack measure selection in virtual network sy...Uvaraj Shan
This document proposes NICE, a framework for secure intrusion detection and attack mitigation in virtual network systems. NICE uses distributed agents on cloud servers to monitor traffic, detect vulnerabilities, and generate attack graphs. It profiles virtual machines to identify their state and vulnerabilities. When potential attacks are detected, NICE can quarantine suspicious VMs and inspect their traffic. The attack analyzer correlates alerts, constructs attack graphs, and selects appropriate countermeasures based on the graphs. Evaluations show NICE can effectively detect attacks while minimizing performance overhead for the cloud system.
FORTIFICATION OF HYBRID INTRUSION DETECTION SYSTEM USING VARIANTS OF NEURAL ...IJNSA Journal
Intrusion Detection Systems (IDS) form a key part of system defence, where it identifies abnormal
activities happening in a computer system. In recent years different soft computing based techniques have
been proposed for the development of IDS. On the other hand, intrusion detection is not yet a perfect
technology. This has provided an opportunity for data mining to make quite a lot of important
contributions in the field of intrusion detection. In this paper we have proposed a new hybrid technique
by utilizing data mining techniques such as fuzzy C means clustering, Fuzzy neural network / Neurofuzzy and radial basis function(RBF) SVM for fortification of the intrusion detection system. The
proposed technique has five major steps in which, first step is to perform the relevance analysis, and then
input data is clustered using Fuzzy C-means clustering. After that, neuro-fuzzy is trained, such that each
of the data point is trained with the corresponding neuro-fuzzy classifier associated with the cluster.
Subsequently, a vector for SVM classification is formed and in the last step, classification using RBF-
SVM is performed to detect intrusion has happened or not. Data set used is the KDD cup 1999 dataset
and we have used precision, recall, F-measure and accuracy as the evaluation metrics parameters. Our
technique could achieve better accuracy for all types of intrusions. The results of proposed technique are
compared with the other existing techniques. These comparisons proved the effectiveness of our
technique.
Three level intrusion detection system based on conditional generative advers...IJECEIAES
Security threat protection is important in the internet of things (IoT) applications since both the connected device and the captured data can be hacked or hijacked or both at the same time. To tackle the above-mentioned problem, we proposed three-level intrusion detection system conditional generative adversarial network (3LIDS-CGAN) model which includes four phases such as first-level intrusion detection system (IDS), second-level IDS, third-level IDS, and attack type classification. In first-level IDS, features of the incoming packets are extracted by the firewall. Based on the extracted features the packets are classified into three classes such as normal, malicious, and suspicious using support vector machine and golden eagle optimization. Suspicious packets are forwarded to the second-level IDS which classified the suspicious packets as normal or malicious. Here, signature-based intrusions are detected using attack history information, and anomaly-based intrusions are detected using event-based semantic mapping. In third-level IDS, adversary packets are detected using CGAN which automatically learns the adversarial environment and detects adversary packets accurately. Finally, proximal policy optimization is proposed to detect the attack type. Experiments are conducted using the NS-3.26 network simulator and performance is evaluated by various performance metrics which results that the proposed 3LIDS-CGAN model outperforming other existing works.
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...IJNSA Journal
This document proposes a hybrid architecture for a distributed intrusion detection system using multiple agents. The key aspects of the architecture include:
- Using multiple independent tracker agents that monitor hosts and generate reports sent to monitors and storage.
- Monitors analyze activity and compare to signatures to detect known attacks, or send data to anomaly detectors.
- Anomaly and misuse detectors use classification and pattern matching to detect known and unknown attacks.
- An inference module coordinates entities across hosts to classify new attacks using a knowledge base and signature generator.
- A countermeasure module alerts administrators and can take actions like dropping packets in response to detected attacks.
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...IJNSA Journal
In order to the rapid growth of the network application, new kinds of network attacks are emerging endlessly. So it is critical to protect the networks from attackers and the Intrusion detection technology becomes popular. Therefore, it is necessary that this security concern must be articulate right from the beginning of the network design and deployment. The intrusion detection technology is the process of identifying network activity that can lead to a compromise of security policy. Lot of work has been done in detection of intruders. But the solutions are not satisfactory. In this paper, we propose a novel Distributed Intrusion Detection System using Multi Agent In order to decrease false alarms and manage misuse and anomaly detects.
Detecting network attacks model based on a convolutional neural network IJECEIAES
Due to the increasing use of networks at present, Internet systems have raised many security problems, and statistics indicate that the rate of attacks or intrusions has increased excessively annually, and in the event of any malicious attack on network vulnerabilities or information systems, it may lead to serious disasters, violating policies on network security, i.e., “confidentiality, integrity, and availability” (CIA). Therefore, many detection systems, such as the intrusion detection system, appeared. In this paper, we built a system that detects network attacks using the latest machine learning algorithms and a convolutional neural network based on a dataset of the CSE-CIC-IDS2018. It is a recent dataset that contains a set of common and recent attacks. The detection rate is 99.7%, distinguishing between aggressive attacks and natural assertiveness.
A technical review and comparative analysis of machine learning techniques fo...IJECEIAES
Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyber attacks at the network-level and the host-level in a timely and automatic manner. However, Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability and accuracy. Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber traffic in real life. Generally Mobile Ad Hoc Networks have given the low physical security for mobile devices, because of the properties such as node mobility, lack of centralized management and limited bandwidth. To tackle these security issues, traditional cryptography schemes can-not completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying Deep learning methods techniques in IDS are capable of adapting the dynamic environments of MANETs and enables the system to make decisions on intrusion while continuing to learn about their mobile environment. An IDS in MANET is a sensoring mechanism that monitors nodes and network activities in order to detect malicious actions and malicious attempt performed by Intruders. Recently, multiple deep learning approaches have been proposed to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of three models, Inceprtion architecture convolutional neural network (Inception-CNN), Bidirectional long short-term memory (BLSTM) and deep belief network (DBN) on the deep learning-based intrusion detection systems, using the NSL-KDD dataset containing information about intrusion and regular network connections, the goal is to provide basic guidance on the choice of deep learning models in MANET.
CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SETIJNSA Journal
This document summarizes research on using various data mining classification techniques to handle false alerts in intrusion detection systems. The researchers tested many data mining procedures on the KDD Cup 99 dataset, including multilayer perceptron neural networks, rule-based models, support vector machines, naive Bayes, and association rule mining. The best accuracy was 92% for multilayer perceptrons, but rule-based models had the fastest training time at 4 seconds. The researchers concluded that different techniques should be used together to handle different types of network attacks.
COPYRIGHTThis thesis is copyright materials protected under the .docxvoversbyobersby
COPYRIGHT
This thesis is copyright materials protected under the Berne Convection, the copyright Act 1999 and other international and national enactments in that behalf, on intellectual property. It may not be reproduced by any means in full or in part except for short extracts in fair dealing so for research or private study, critical scholarly review or discourse with acknowledgment, with written permission of the Dean School of Graduate Studies on behalf of both the author and XXX XXX University.ABSTRACT
With Fast growing internet world the risk of intrusion has also increased, as a result Intrusion Detection System (IDS) is the admired key research field. IDS are used to identify any suspicious activity or patterns in the network or machine, which endeavors the security features or compromise the machine. IDS majorly use all the features of the data. It is a keen observation that all the features are not of equal relevance for the detection of attacks. Moreover every feature does not contribute in enhancing the system performance significantly. The main aim of the work done is to develop an efficient denial of service network intrusion classification model. The specific objectives included: to analyse existing literature in intrusion detection systems; what are the techniques used to model IDS, types of network attacks, performance of various machine learning tools, how are network intrusion detection systems assessed; to find out top network traffic attributes that can be used to model denial of service intrusion detection; to develop a machine learning model for detection of denial of service network intrusion.Methods: The research design was experimental and data was collected by simulation using NSL-KDD dataset. By implementing Correlation Feature Selection (CFS) mechanism using three search algorithms, a smallest set of features is selected with all the features that are selected very frequently. Findings: The smallest subset of features chosen is the most nominal among all the feature subset found. Further, the performances using Artificial neural networks(ANN), decision trees, Support Vector Machines (SVM) and K-Nearest Neighbour (KNN) classifiers is compared for 7 subsets found by filter model and 41 attributes. Results: The outcome indicates a remarkable improvement in the performance metrics used for comparison of the two classifiers. The results show that using 17/18 selected features improves DOS types classification accuracies as compared to using the 41 features in the NSL-KDD dataset. It was further observed that using an ensemble of three classifiers with decision fusion performs better as compared to using a single classifier for DOS type’s classification. Among machine learning tools experimented, ANN achieved best classification accuracies followed by SVM and DT. KNN registered the lowest classification accuracies. Application: The proposed work with such an improved detection rate and lesser classification time and lar.
In recent years, wireless sensor network (WSN) is used in several application areas resembling observance, tracking, and dominant in IoTs. for several applications of WSN, security is a crucial demand. However, security solutions in WSN disagree from ancient networks because of resource limitation and process constraints. This paper analyzes security solutions: TinySec, IEEE 802.15.4, SPINS, MiniSEC, LSec, LLSP, LISA, and LISP in WSN. This paper additionally presents characteristics, security needs, attacks, cryptography algorithms, and operation modes. This paper is taken into account to be helpful for security designers in WSNs.
Tactical approach to identify and quarantine spurious node participation requ...IJECEIAES
Securing Wireless Sensor Network (WSN) from variable forms of adversary is still an open end challenge. Review of diversified security apprroaches towards such problems that they are highly symptomatic with respect to resiliency strength against attack. Therefore, the proposed system highlights a novel and effective solution that is capable of identify the spurios request for participating in teh network building process from attacker and in return could deviate the route of attacker to some virtual nodes and links. A simple trust based mechanism is constructed for validating the legitimacy of such request generated from adversary node. The proposed system not only presents a security solution but also assists in enhancing the routing process significantly. The simulated outcome of the study shows that proposed system offers significantly good energy conservation, satisfactory data forwarding performance, reduced processing time in contrast to existing standard security practices.
The document summarizes a research paper that proposes a novel tactic approach to identify and quarantine spurious node participation requests in wireless sensor network applications. The approach aims to address the open challenge of securing wireless sensor networks from various forms of adversary attacks. It highlights a simple trust-based mechanism to validate legitimacy of requests from attacker nodes and divert their routes to virtual nodes/links. The simulated results of the proposed approach show that it offers significantly better energy conservation, data forwarding performance, and reduced processing time compared to existing standard security practices.
MACHINE LEARNING AND DEEP LEARNING MODEL-BASED DETECTION OF IOT BOTNET ATTACKS.IRJET Journal
This document discusses machine learning and deep learning models for detecting IoT botnet attacks. It begins with an abstract that outlines the challenges of securing the growing number of IoT devices and describes how machine learning and deep learning techniques like LSTM RNN can be used to develop effective detection systems. The introduction provides background on botnets, distributed denial of service attacks, and the need for detection systems. The literature review then summarizes several previous works that used techniques such as Bayesian classifiers, random neural networks, decision trees, and other machine learning algorithms for attack detection. The methodology section outlines the general approach of anomaly-based intrusion detection systems and different learning methods. The experimental setup describes collecting and preprocessing data, feature extraction, model training and evaluation
Intrusion Detection System Using Machine Learning: An OverviewIRJET Journal
This document provides an overview of machine learning approaches for intrusion detection systems (IDS). It discusses how IDS use data mining techniques like classification, clustering, and association rule mining to detect network intrusions based on patterns in data. The document reviews several papers applying methods like ant colony optimization, support vector machines, genetic algorithms, and convolutional neural networks to classify network activities as normal or intrusive. It compares the strengths and limitations of different machine learning algorithms for IDS and identifies areas for potential improvement in future research.
Secure intrusion detection and countermeasure selection in virtual system usi...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Data mining approach to analyzing intrusion detection of wireless sensor networknooriasukmaningtyas
Wireless sensor network (WSN) is a collection of wireless sensor nodes
which are distributed in nature and a base station where the dispersed nodes
are used to monitor and the physical conditions of the environment is
recorded and then these data are organized into the base. Its application has
been reached out from critical military application such as battlefield
surveillance to traffic, health, industrial areas, intruder detection, security and
surveillance. Due to various features in WSN it is very prone to various types
external attacks. Preventing such attacks, intrusion detection system (IDS) is
very important so that attacker cannot steal or manipulate data. Data mining
is a technique that can help to discover patterns in large dataset. This paper
proposed a data mining technique for different types of classification
algorithms to detect denial of service (DoS) attacks which is of four types.
They are Grayhole, Blackhole, Flooding and TDMA. A number of data
mining techniques, such as KNN, Naïve Bayes, Logistic Regression, support
vector machine (SVM) and ANN algorithms are applied on the dataset and
analyze their performance in detecting the attacks. The analysis reveals the
applicability of these algorithms for detecting and predicting such attacks and
can be recommended for network specialist and analysts.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
An approach for ids by combining svm and ant colony algorithmeSAT Journals
This summarizes a research paper that proposes a new approach called CSVAC (Combined Support Vector with Ant Colony) for intrusion detection. The approach combines two algorithms, Support Vector Machine (SVM) and Ant Colony Optimization (ACO), to classify network data as normal or abnormal. SVM is used to generate a separating hyperplane and find support vectors, while ACO performs clustering around the support vectors. The clusters are added to the SVM training set and it is retrained in an iterative process until the detection rate exceeds a threshold. The paper evaluates this approach on the standard KDD99 dataset and finds it achieves superior results to other algorithms in terms of accuracy and efficiency.
Similar to Hybrid Technique for Detection of Denial of Service (DOS) Attack in Wireless Sensor Network (20)
Content-Based Image Retrieval (CBIR) systems have been used for the searching of relevant images in various research areas. In CBIR systems features such as shape, texture and color are used. The extraction of features is the main step on which the retrieval results depend. Color features in CBIR are used as in the color histogram, color moments, conventional color correlogram and color histogram. Color space selection is used to represent the information of color of the pixels of the query image. The shape is the basic characteristic of segmented regions of an image. Different methods are introduced for better retrieval using different shape representation techniques; earlier the global shape representations were used but with time moved towards local shape representations. The local shape is more related to the expressing of result instead of the method. Local shape features may be derived from the texture properties and the color derivatives. Texture features have been used for images of documents, segmentation-based recognition,and satellite images. Texture features are used in different CBIR systems along with color, shape, geometrical structure and sift features.
This document discusses clickjacking attacks, which hijack users' clicks to perform unintended actions. It provides an overview of clickjacking, describes different types of attacks, and analyzes vulnerabilities that make websites susceptible. Experiments are conducted on a sample social networking site, applying various clickjacking techniques. Potential defenses are tested, including X-Frame-Options headers and frame busting code. A proposed solution detects transparent iframes to warn users and check for hidden mouse pointers to mitigate cursorjacking. Analysis of top Jammu and Kashmir websites found most were vulnerable, while browser behavior studies showed varying support for defenses.
Performance Analysis of Audio and Video Synchronization using Spreaded Code D...Eswar Publications
The audio and video synchronization plays an important role in speech recognition and multimedia communication. The audio-video sync is a quite significant problem in live video conferencing. It is due to use of various hardware components which introduces variable delay and software environments. The objective of the synchronization is used to preserve the temporal alignment between the audio and video signals. This paper proposes the audio-video synchronization using spreading codes delay measurement technique. The performance of the proposed method made on home database and achieves 99% synchronization efficiency. The audio-visual
signature technique provides a significant reduction in audio-video sync problems and the performance analysis of audio and video synchronization in an effective way. This paper also implements an audio- video synchronizer and analyses its performance in an efficient manner by synchronization efficiency, audio-video time drift and audio-video delay parameters. The simulation result is carried out using mat lab simulation tools and simulink. It is automatically estimating and correcting the timing relationship between the audio and video signals and maintaining the Quality of Service.
Due to the availability of complicated devices in industry, models for consumers at lower cost of resources are developed. Home Automation systems have been developed by several researchers. The limitations of home automation includes complexity in architecture, higher costs of the equipment, interface inflexibility. In this paper as we have proposed, the working protocol of PIC 16F72 technology is which is secure, cost efficient, flexible that leads to the development of efficient home automation systems. The system is operational to control various home appliances like fans, Bulbs, Tube light. The following paper describes about components used and working of all components connected. The home automation system makes use of Android app entitled “Home App” which gives
flexibility and easy to use GUI.
Semantically Enchanced Personalised Adaptive E-Learning for General and Dysle...Eswar Publications
E-learning plays an important role in providing required and well formed knowledge to a learner. The medium of e- learning has achieved advancement in various fields such as adaptive e-learning systems. The need for enhancing e-learning semantically can enhance the retrieval and adaptability of the learning curriculum. This paper provides a semantically enhanced module based e-learning for computer science programme on a learnercentric perspective. The learners are categorized based on their proficiency for providing personalized learning environment for users. Learning disorders on the platform of e-learning still require lots of research. Therefore, this paper also provides a personalized assessment theoretical model for alphabet learning with learning objects for
children’s who face dyslexia.
Agriculture plays an important role in the economy of our country. Over 58 percent of the rural households depend on the agriculture sector as their means of livelihood. Agriculture is one of the major contributors to Gross Domestic Product(GDP). Seeds are the soul of agriculture. This application helps in reducing the time for the researchers as well as farmers to know the seedling parameters. The application helps the farmers to know about the percentage of seedlings that will grow and it is very essential in estimating the yield of that particular crop. Manual calculation may lead to some error, to minimize that error, the developed app is used. The scientist and farmers require the app to know about the physiological seed quality parameters and to take decisions regarding their farming activities. In this article a desktop app for seed germination percentage and vigour index calculation are developed in PHP scripting language.
What happens when adaptive video streaming players compete in time-varying ba...Eswar Publications
Competition among adaptive video streaming players severely diminishes user-QoE. When players compete at a bottleneck link many do not obtain adequate resources. This imbalance eventually causes ill effects such as screen flickering and video stalling. There have been many attempts in recent years to overcome some of these problems. However, added to the competition at the bottleneck link there is also the possibility of varying network bandwidth which can make the situation even worse. This work focuses on such a situation. It evaluates current heuristic adaptive video players at a bottleneck link with time-varying bandwidth conditions. Experimental setup includes the TAPAS player and emulated network conditions. The results show PANDA outperforms FESTIVE, ELASTIC and the Conventional players.
WLI-FCM and Artificial Neural Network Based Cloud Intrusion Detection SystemEswar Publications
Security and Performance aspects of cloud computing are the major issues which have to be tended to in Cloud Computing. Intrusion is one such basic and imperative security problem for Cloud Computing. Consequently, it is essential to create an Intrusion Detection System (IDS) to detect both inside and outside assaults with high detection precision in cloud environment. In this paper, cloud intrusion detection system at hypervisor layer is developed and assesses to detect the depraved activities in cloud computing environment. The cloud intrusion detection system uses a hybrid algorithm which is a fusion of WLI- FCM clustering algorithm and Back propagation artificial Neural Network to improve the detection accuracy of the cloud intrusion detection system. The proposed system is implemented and compared with K-means and classic FCM. The DARPA’s KDD cup dataset 1999 is used for simulation. From the detailed performance analysis, it is clear that the proposed system is able to detect the anomalies with high detection accuracy and low false alarm rate.
Spreading Trade Union Activities through Cyberspace: A Case StudyEswar Publications
This report present the outcome of an investigative research conducted to examine the modu-operandi of academic staff union of polytechnics (ASUP) YabaTech. The investigation covered the logistics and cost implication for spreading union activities among members. It was discovered that cost of management and dissemination of information to members was at high side, also logistics problem constitutes to loss of information in transit hence cut away some members from union activities. To curtail the problem identified, we proposed the
design of secure and dynamic website for spreading union activities among members and public. The proposed system was implemented using HTML5 technology, interface frameworks like Bootstrap and j query which enables the responsive feature of the application interface. The backend was designed using PHPMYSQL. It was discovered from the evaluation of the new system that cost of managing information has reduced considerably, and logistic problems identified in the old system has become a forgotten issue.
Identifying an Appropriate Model for Information Systems Integration in the O...Eswar Publications
Nowadays organizations are using information systems for optimizing processes in order to increase coordination and interoperability across the organizations. Since Oil and Gas Industry is one of the large industries in whole of the world, there is a need to compatibility of its Information Systems (IS) which consists three categories of systems: Field IS, Plant IS and Enterprise IS to create interoperability and approach the
optimizing processes as its result. In this paper we introduce the different models of information systems integration, identify the types of information systems that are using in the upstream and downstream sectors of petroleum industry, and finally based on expert’s opinions will identify a suitable model for information systems integration in this industry.
Link-and Node-Disjoint Evaluation of the Ad Hoc on Demand Multi-path Distance...Eswar Publications
This work illustrates the AOMDV routing protocol. Its ancestor, the AODV routing protocol is also described. This tutorial demonstrates how forward and reverse paths are created by the AOMDV routing protocol. Loop free paths formulation is described, together with node and link disjoint paths. Finally, the performance of the AOMDV routing protocol is investigated along link and node disjoint paths. The WSN with the AOMDV routing protocol using link disjoint paths is better than the WSN with the AOMDV routing protocol using node disjoint paths for energy consumption.
Bridging Centrality: Identifying Bridging Nodes in Transportation NetworkEswar Publications
To identify the importance of node of a network, several centralities are used. Majority of these centrality measures are dominated by components' degree due to their nature of looking at networks’ topology. We propose a centrality to identification model, bridging centrality, based on information flow and topological aspects. We apply bridging centrality on real world networks including the transportation network and show that the nodes distinguished by bridging centrality are well located on the connecting positions between highly connected regions. Bridging centrality can discriminate bridging nodes, the nodes with more information flowed through them and locations between highly connected regions, while other centrality measures cannot.
Now a days we are living in an era of Information Technology where each and every person has to become IT incumbent either intentionally or unintentionally. Technology plays a vital role in our day to day life since last few decades and somehow we all are depending on it in order to obtain maximum benefit and comfort. This new era equipped with latest advents of technology, enlightening world in the form of Internet of Things (IoT). Internet of things is such a specified and dignified domain which leads us to the real world scenarios where each object can perform some task while communicating with some other objects. The world with full of devices, sensors and other objects which will communicate and make human life far better and easier than ever. This paper provides an overview of current research work on IoT in terms of architecture, a technology used and applications. It also highlights all the issues related to technologies used for IoT, after the literature review of research work. The main purpose of this survey is to provide all the latest technologies, their corresponding
trends and details in the field of IoT in systematic manner. It will be helpful for further research.
Automatic Monitoring of Soil Moisture and Controlling of Irrigation SystemEswar Publications
In past couple of decades, there is immediate growth in field of agricultural technology. Utilization of proper method of irrigation by drip is very reasonable and proficient. A various drip irrigation methods have been proposed, but they have been found to be very luxurious and dense to use. The farmer has to maintain watch on irrigation schedule in the conventional drip irrigation system, which is different for different types of crops. In remotely monitored embedded system for irrigation purposes have become a new essential for farmer to accumulate his energy, time and money and will take place only when there will be requirement of water. In this approach, the soil test for chemical constituents, water content, and salinity and fertilizer requirement data collected by wireless and processed for better drip irrigation plan. This paper reviews different monitoring systems and proposes an automatic monitoring system model using Wireless Sensor Network (WSN) which helps the farmer to improve the yield.
Multi- Level Data Security Model for Big Data on Public Cloud: A New ModelEswar Publications
With the advent of cloud computing the big data has emerged as a very crucial technology. The certain type of cloud provides the consumers with the free services like storage, computational power etc. This paper is intended to make use of infrastructure as a service where the storage service from the public cloud providers is going to leveraged by an individual or organization. The paper will emphasize the model which can be used by anyone without any cost. They can store the confidential data without any type of security issue, as the data will be altered
in such a way that it cannot be understood by the intruder if any. Not only that but the user can retrieve back the original data within no time. The proposed security model is going to effectively and efficiently provide a robust security while data is on cloud infrastructure as well as when data is getting migrated towards cloud infrastructure or vice versa.
Impact of Technology on E-Banking; Cameroon PerspectivesEswar Publications
The financial services industry is experiencing rapid changes in services delivery and channels usage, and financial companies and users of financial services are looking at new technologies as they emerge and deciding whether or not to embrace them and the new opportunities to save and manage enormous time, cost and stress.
There is no doubt about the favourable and manifold impact of technology on e-banking as pictured in this review paper, almost all banks are with the least and most access e-banking Technological equipments like ATMs and Cards. On the other Hand cheap and readily available technology has opened a favourable competition in ebanking services business with a lot of wide range competitors competing with Commercial Banks in Cameroon in providing digital financial services.
Classification Algorithms with Attribute Selection: an evaluation study using...Eswar Publications
Attribute or feature selection plays an important role in the process of data mining. In general the data set contains more number of attributes. But in the process of effective classification not all attributes are relevant.
Attribute selection is a technique used to extract the ranking of attributes. Therefore, this paper presents a comparative evaluation study of classification algorithms before and after attribute selection using Waikato Environment for Knowledge Analysis (WEKA). The evaluation study concludes that the performance metrics of the classification algorithm, improves after performing attribute selection. This will reduce the work of processing irrelevant attributes.
Mining Frequent Patterns and Associations from the Smart meters using Bayesia...Eswar Publications
In today’s world migration of people from rural areas to urban areas is quite common. Health care services are one of the most challenging aspect that is must require to the people with abnormal health. Advancements in the technologies lead to build the smart homes, which contains various sensor or smart meter devices to automate the process of other electronic device. Additionally these smart meters can be able to capture the daily activities of the patients and also monitor the health conditions of the patients by mining the frequent patterns and
association rules generated from the smart meters. In this work we proposed a model that is able to monitor the activities of the patients in home and can send the daily activities to the corresponding doctor. We can extract the frequent patterns and association rules from the log data and can predict the health conditions of the patients and can give the suggestions according to the prediction. Our work is divided in to three stages. Firstly, we used to record the daily activities of the patient using a specific time period at three regular intervals. Secondly we applied the frequent pattern growth for extracting the association rules from the log file. Finally, we applied k means clustering for the input and applied Bayesian network model to predict the health behavior of the patient and precautions will be given accordingly.
Network as a Service Model in Cloud Authentication by HMAC AlgorithmEswar Publications
Resource pooling on internet-based accessing on use as pay environmental technology and ruled in IT field is the
cloud. Present, in every organization has trusted the web, however, the information must flow but not hold the
data. Therefore, all customers have to use the cloud. While the cloud progressing info by securing-protocols. Third
party observing and certain circumstances directly stale in flow and kept of packets in the virtual private cloud.
Global security statistics in the year 2017, hacking sensitive information in cloud approximately maybe 75.35%,
and the world security analyzer said this calculation maybe reached to 100%. For this cause, this proposed
research work concentrates on Authentication-Message-Digest-Key with authentication in routing the Network as
a Service of packets in OSPF (Open Shortest Path First) implementing Cloud with GNS3 has tested them to
securing from attackers.
Microstrip patch antennas are recently used in wireless detection applications due to their low power consumption, low cost, versatility, field excitation, ease of fabrication etc. The microstrip patch antennas are also called as printed antennas which is suffer with an array elements of antenna and narrow bandwidth. To overcome the above drawbacks, Flame Retardant Material is used as the substrate. Rectangular shape of microstrip patch antenna with FR4 material as the substrate which is more suitable for the explosive detection applications. The proposed printed antenna was designed with the dimension of 60 x 60 mm2. FR-4 material has a dielectric constant value of 4.3 with thickness 1.56 mm, length and width 60 mm and 60 mm respectively. One side of the substrate contains the ground plane of dimensions 60 x60 mm2 made of copper and the other side of the substrate contains the patch which have dimensions 34 x 29 mm2 and thickness 0.03mm which is also made of copper. RMPA without slot, Vertical slot RMPA, Double horizontal slot RMPA and Centre slot RMPA structures were
designed and the performance of the antennas were analysed with various parameters such as gain, directivity, Efield, VSWR and return loss. From the performance analysis, double horizontal slot RMPA antenna provides a better result and it provides maximum gain (8.61dB) and minimum return loss (-33.918dB). Based on the E-field excitation value the SEMTEX explosive material is detected and it was simulated using CST software.
Transcript: Details of description part II: Describing images in practice - T...BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and slides: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
TrustArc Webinar - 2024 Data Privacy Trends: A Mid-Year Check-InTrustArc
Six months into 2024, and it is clear the privacy ecosystem takes no days off!! Regulators continue to implement and enforce new regulations, businesses strive to meet requirements, and technology advances like AI have privacy professionals scratching their heads about managing risk.
What can we learn about the first six months of data privacy trends and events in 2024? How should this inform your privacy program management for the rest of the year?
Join TrustArc, Goodwin, and Snyk privacy experts as they discuss the changes we’ve seen in the first half of 2024 and gain insight into the concrete, actionable steps you can take to up-level your privacy program in the second half of the year.
This webinar will review:
- Key changes to privacy regulations in 2024
- Key themes in privacy and data governance in 2024
- How to maximize your privacy program in the second half of 2024
Implementations of Fused Deposition Modeling in real worldEmerging Tech
The presentation showcases the diverse real-world applications of Fused Deposition Modeling (FDM) across multiple industries:
1. **Manufacturing**: FDM is utilized in manufacturing for rapid prototyping, creating custom tools and fixtures, and producing functional end-use parts. Companies leverage its cost-effectiveness and flexibility to streamline production processes.
2. **Medical**: In the medical field, FDM is used to create patient-specific anatomical models, surgical guides, and prosthetics. Its ability to produce precise and biocompatible parts supports advancements in personalized healthcare solutions.
3. **Education**: FDM plays a crucial role in education by enabling students to learn about design and engineering through hands-on 3D printing projects. It promotes innovation and practical skill development in STEM disciplines.
4. **Science**: Researchers use FDM to prototype equipment for scientific experiments, build custom laboratory tools, and create models for visualization and testing purposes. It facilitates rapid iteration and customization in scientific endeavors.
5. **Automotive**: Automotive manufacturers employ FDM for prototyping vehicle components, tooling for assembly lines, and customized parts. It speeds up the design validation process and enhances efficiency in automotive engineering.
6. **Consumer Electronics**: FDM is utilized in consumer electronics for designing and prototyping product enclosures, casings, and internal components. It enables rapid iteration and customization to meet evolving consumer demands.
7. **Robotics**: Robotics engineers leverage FDM to prototype robot parts, create lightweight and durable components, and customize robot designs for specific applications. It supports innovation and optimization in robotic systems.
8. **Aerospace**: In aerospace, FDM is used to manufacture lightweight parts, complex geometries, and prototypes of aircraft components. It contributes to cost reduction, faster production cycles, and weight savings in aerospace engineering.
9. **Architecture**: Architects utilize FDM for creating detailed architectural models, prototypes of building components, and intricate designs. It aids in visualizing concepts, testing structural integrity, and communicating design ideas effectively.
Each industry example demonstrates how FDM enhances innovation, accelerates product development, and addresses specific challenges through advanced manufacturing capabilities.
Kief Morris rethinks the infrastructure code delivery lifecycle, advocating for a shift towards composable infrastructure systems. We should shift to designing around deployable components rather than code modules, use more useful levels of abstraction, and drive design and deployment from applications rather than bottom-up, monolithic architecture and delivery.
Fluttercon 2024: Showing that you care about security - OpenSSF Scorecards fo...Chris Swan
Have you noticed the OpenSSF Scorecard badges on the official Dart and Flutter repos? It's Google's way of showing that they care about security. Practices such as pinning dependencies, branch protection, required reviews, continuous integration tests etc. are measured to provide a score and accompanying badge.
You can do the same for your projects, and this presentation will show you how, with an emphasis on the unique challenges that come up when working with Dart and Flutter.
The session will provide a walkthrough of the steps involved in securing a first repository, and then what it takes to repeat that process across an organization with multiple repos. It will also look at the ongoing maintenance involved once scorecards have been implemented, and how aspects of that maintenance can be better automated to minimize toil.
How RPA Help in the Transportation and Logistics Industry.pptxSynapseIndia
Revolutionize your transportation processes with our cutting-edge RPA software. Automate repetitive tasks, reduce costs, and enhance efficiency in the logistics sector with our advanced solutions.
Choose our Linux Web Hosting for a seamless and successful online presencerajancomputerfbd
Our Linux Web Hosting plans offer unbeatable performance, security, and scalability, ensuring your website runs smoothly and efficiently.
Visit- https://onliveserver.com/linux-web-hosting/
Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Em...Erasmo Purificato
Slide of the tutorial entitled "Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Emerging Trends" held at UMAP'24: 32nd ACM Conference on User Modeling, Adaptation and Personalization (July 1, 2024 | Cagliari, Italy)
Advanced Techniques for Cyber Security Analysis and Anomaly DetectionBert Blevins
Cybersecurity is a major concern in today's connected digital world. Threats to organizations are constantly evolving and have the potential to compromise sensitive information, disrupt operations, and lead to significant financial losses. Traditional cybersecurity techniques often fall short against modern attackers. Therefore, advanced techniques for cyber security analysis and anomaly detection are essential for protecting digital assets. This blog explores these cutting-edge methods, providing a comprehensive overview of their application and importance.
BT & Neo4j: Knowledge Graphs for Critical Enterprise Systems.pptx.pdfNeo4j
Presented at Gartner Data & Analytics, London Maty 2024. BT Group has used the Neo4j Graph Database to enable impressive digital transformation programs over the last 6 years. By re-imagining their operational support systems to adopt self-serve and data lead principles they have substantially reduced the number of applications and complexity of their operations. The result has been a substantial reduction in risk and costs while improving time to value, innovation, and process automation. Join this session to hear their story, the lessons they learned along the way and how their future innovation plans include the exploration of uses of EKG + Generative AI.
RPA In Healthcare Benefits, Use Case, Trend And Challenges 2024.pptxSynapseIndia
Your comprehensive guide to RPA in healthcare for 2024. Explore the benefits, use cases, and emerging trends of robotic process automation. Understand the challenges and prepare for the future of healthcare automation
Support en anglais diffusé lors de l'événement 100% IA organisé dans les locaux parisiens d'Iguane Solutions, le mardi 2 juillet 2024 :
- Présentation de notre plateforme IA plug and play : ses fonctionnalités avancées, telles que son interface utilisateur intuitive, son copilot puissant et des outils de monitoring performants.
- REX client : Cyril Janssens, CTO d’ easybourse, partage son expérience d’utilisation de notre plateforme IA plug & play.
Understanding Insider Security Threats: Types, Examples, Effects, and Mitigat...Bert Blevins
Today’s digitally connected world presents a wide range of security challenges for enterprises. Insider security threats are particularly noteworthy because they have the potential to cause significant harm. Unlike external threats, insider risks originate from within the company, making them more subtle and challenging to identify. This blog aims to provide a comprehensive understanding of insider security threats, including their types, examples, effects, and mitigation techniques.
Blockchain technology is transforming industries and reshaping the way we conduct business, manage data, and secure transactions. Whether you're new to blockchain or looking to deepen your knowledge, our guidebook, "Blockchain for Dummies", is your ultimate resource.
Are you interested in dipping your toes in the cloud native observability waters, but as an engineer you are not sure where to get started with tracing problems through your microservices and application landscapes on Kubernetes? Then this is the session for you, where we take you on your first steps in an active open-source project that offers a buffet of languages, challenges, and opportunities for getting started with telemetry data.
The project is called openTelemetry, but before diving into the specifics, we’ll start with de-mystifying key concepts and terms such as observability, telemetry, instrumentation, cardinality, percentile to lay a foundation. After understanding the nuts and bolts of observability and distributed traces, we’ll explore the openTelemetry community; its Special Interest Groups (SIGs), repositories, and how to become not only an end-user, but possibly a contributor.We will wrap up with an overview of the components in this project, such as the Collector, the OpenTelemetry protocol (OTLP), its APIs, and its SDKs.
Attendees will leave with an understanding of key observability concepts, become grounded in distributed tracing terminology, be aware of the components of openTelemetry, and know how to take their first steps to an open-source contribution!
Key Takeaways: Open source, vendor neutral instrumentation is an exciting new reality as the industry standardizes on openTelemetry for observability. OpenTelemetry is on a mission to enable effective observability by making high-quality, portable telemetry ubiquitous. The world of observability and monitoring today has a steep learning curve and in order to achieve ubiquity, the project would benefit from growing our contributor community.
Quantum Communications Q&A with Gemini LLM. These are based on Shannon's Noisy channel Theorem and offers how the classical theory applies to the quantum world.
Recent Advancements in the NIST-JARVIS Infrastructure
Hybrid Technique for Detection of Denial of Service (DOS) Attack in Wireless Sensor Network
1. Int. J. Advanced Networking and Applications
Volume: 07 Issue: 02 Pages: 2674-2681 (2015) ISSN: 0975-0290
2674
Hybrid Technique for Detection of Denial of
Service (DOS) Attack in Wireless Sensor
Network
S.Sumitha Pandit1
1
Research Scholar, Department Of Computer Science, Avinashilingam Institute for Home Science and Higher
Education for Women, Coimbatore, TamilNadu, India
Email: sumitha975@gmail.com
Dr.B.Kalpana2
2
Professor, Department Of Computer Science, Avinashilingam Institute for Home Science and Higher Education for
Women, Coimbatore, TamilNadu, India
Email: kalpanacsekar@gmail.com
-----------------------------------------------------------------------ABSTRACT-----------------------------------------------------------
Wireless Sensor Network (WSNs) are deployed at aggressive environments which are vulnerable to various
security attacks such as Wormholes, Denial of Attacks and Sybil Attacks. There are various intrusion detection
techniques that are used to identify attacks in a network with high accuracy level. This paper has focused on
Denial of Service attack, since it is the most common attack that affects the environment severely. Therefore a new
hybrid technique combining Hidden Markov Model with Ant Colony Optimization (HMM+ACO) has been
proposed that gives improved performance than the other techniques.
Keywords – ACO, Denial of Service, HMM ,Intrusion Detection,Wireless Sensor Network
------------------------------------------------------------------------------------------------------------------------------------------------
Date of Submission: Sep 17, 2015 Date of Acceptance: Oct 09, 2015
------------------------------------------------------------------------------------------------------------------------------------------------
1.INTRODUCTION
1.1 Wireless Sensor Network (WSN)
Wireless Sensor Networks have been widely
applied in various fields such as environmental
monitoring, healthcare management, battlefield
surveillance and industry control. Wireless Sensor
Networks (WSNs) is one of the most important
technologies for the twenty-first century. Wireless sensor
network (WSN) connects the distributed autonomous
sensors for collecting the data from sensors or distribute
the data into sensors. Wireless sensor networks (WSNs)
consist of a large number of low-cost, low-power, and
multi-functional sensor nodes. These sensor nodes are
small in size, equipped with sensors, embedded
microprocessors, and radio transceivers [12]. They
communicate over a short distance and collaborate to
accomplish any task. The aim of security mechanism in
WSN is to guard the information from attacks. This
security mechanism which is provided for wireless sensor
network makes sure that network services are available in
presence of any vulnerability. There are security
mechanism is based on five principles [17]
confidentiality, authenticity, Integrity, availability and
data freshness.
To cope up with the attacks, the concept of
Intrusion detection was invented by James Anderson in
1980 and a method based on this was introduced by
Denning in 1987.Intrusion Detection System (IDS) is a
device or software application that monitors the activities
to identify malicious behavior or suspicious event in
different environment. Intrusion Detection System is a
type of sensor that raise the alarm when specific event
occurs and it produces the log report to the management
system. Intrusions are caused by inside attackers and
authorized users attempting to gain and misuse
unauthorized privileges [9]. The various causes of
intrusions include incorrect algorithms, architectures,
vulnerabilities/flaws, implementation mistakes,
component defects and external disturbances [2]. There
are different security attacks in WSN are as follows,
Denial-of-Service (DoS) attacks
Sinkhole/Black hole attacks
Selective forwarding attack
Node Replication attacks
HELLO Flood attacks
Wormhole attacks
Sybil attack
1.2 Denial of Service
The Denial of Service (DoS) attack
frequently sends unwanted packets and it tries to utilize
the bandwidth of network. It legitimates the network user
from accessing the system or resources when required.
The DoS attack can present itself in physical layer, link
layer, network layer and transport layer. DoS attack can
be prevented by strong authentication and identification
built into the intrusion detection system. Other DoS
attacks are very harsh and it reacts in two ways such as
jamming and tampering.
Jamming is the deliberate interference
on the wireless communication channel. This attack is a
2. Int. J. Advanced Networking and Applications
Volume: 07 Issue: 02 Pages: 2674-2681 (2015) ISSN: 0975-0290
2675
common one in which the attacker tries to disrupt the
operations of entire network or a particular small portion
of it. Jamming may be consistent or irregular. To handle
jamming at network layer deals with mapping jamming
area in the network or in neighboring routing area. The
attack is simple and effective when the network is based
on single frequency otherwise the attack should be
eliminated since it uses various forms of spread spectrum.
Tampering is one of the physical
attacks, which targets the hardware of the sensor nodes.
Tampering attack is not feasible to manage hundreds of
nodes extend over an area of several kilometers.
Tampering attackers may dig out the sensitive information
like cryptographic key from node by damaging it to get
access to higher level of communication. The only
security mechanism against tempering is to temper-proof
physical packaging. But it costs additional. [1]
2. RELATED WORK
Shi-Jinn Horng et al [26] have designed a new
flow for intrusion detection system using SVM technique.
The famous KDD Cup 1999 dataset was used to evaluate
the proposed system. Compared with other intrusion
detection systems that are based on the same dataset, this
system showed better performance in the detection of DoS
and Probe attacks, and the best performance in overall
accuracy.
Hayoung Oh et al [5] have proposed a real-time
intrusion and anomaly detection system using SOM. This
system labels the map produced by SOM using
correlations between features. It classifies neurons as
normal or attacks. In the case of attack neurons, they have
classified them again into the types of attacks. When a
malicious behavior is caught, this system detects the
intrusion as previously known attack or a new untrained
attack.
Mohammad Wazid [10] has used hybrid anomaly
detection technique with the k-means clustering. WSN are
simulated using OPNET simulator and the resultant dataset
consists of traffic data with end to end delay data which has
been clustered using WEKA 3.6. In this experiment, it has
been observed that two types of anomalies (misdirection
and black hole attacks) are activated in the network.
Shun-Sheng Wang et al [18] have designed an
integrated intrusion detection system using intrusion
dataset from UCI repository .The dataset trained well
using BPN and the output is used as an important
parameter in ART model to cluster the data. Finally the
outputs received from both techniques are compared and
the ART model provides the best accuracy rate and
overall performance.
Mohit Malik et al [11] have applied the rule
based technique for detecting the security attack in WSN.
They have discovered ten important security attack type in
their work and the parameters of those attack have been
developed fuzzy rule based system for calculating the
impact of security attack on the wireless sensor network.
Once the system has been executed it shows the impact of
attack in the network.
Reda M. Elbasiony et al [14] have proposed a
hybrid detection framework i.e. in anomaly detection, k-
means clustering algorithm is used to detect novel
intrusions by clustering the network connection’s data to
collect the most of intrusions together in one or more
clusters. In this proposed hybrid framework, the anomaly
part are improved by replacing the k-means algorithm
with another one called weighted k-means algorithm, In
this approaches Knowledge Discovery and Data Mining
(KDD’99) datasets are used.
LeventKoc et al [7] have proposed a new
technique HNB model which exhibits a superior overall
performance in terms of accuracy, error rate and
misclassification cost .In early stages the traditional Naïve
Bayes model are used but the result produced by HNB is
better than traditional Naïve Bayes. The results they have
produced indicate that this model significantly improves
the accuracy of detecting denial-of-services (DoS) attacks.
WenyingFenga et al [22] have introduced a new
way of combining algorithm for the better result in
detecting intrusions. They have classified the network
activities into normal or abnormal by reducing the
misclassification rate. In this work the author combined
Support Vector Machine method and the Clustering based
on Self-Organized Ant Colony Network to take the
advantages by avoiding their weaknesses. This
Experiments show that CSVAC (Combining Support
Vectors with Ant Colony) outperforms better the SVM or
CSOACN in terms of both classification rate and run-time
efficiency.
MeghaBandgaret al [8] have described a novel
approach using Hidden Markov Models (HMM) to detect
Internet attacks and they have described about an
intrusion detection system for detecting a signature based
attack. They have performed single and multiple HMM
model for source separation both on IP and port
information of source and destination. In this approach
they have reduced the false positive rate.
Dat Tran et al [3] have proposed Fuzzy Gaussian
mixture modeling method for network anomaly detection.
In this work a mixture of Gaussian distributions are used
to represent the network data in multi-dimensional feature
space. Using fuzzy C-means estimation, Gaussian
parameters were estimated and the whole work is carried
out with the KDD Cup data set. The proposed method
produced here is more effective than the vector
quantization method.
VahidGolmah [19] has been developed a hybrid
technique using C5.0 and SVM algorithm and they have
investigated and evaluate the performance of this hybrid
technique with DARPA dataset. The motivation for using
this hybrid approach is to improve the accuracy of the
intrusion detection system when compared to using
individual SVM and C50. By combining the SVM and
C5.0 this technique took less of execution time.
PunamMulak [13] has used hybrid technique by
combining Boundary cutting algorithm and clustering
algorithm. The motivation for using this hybrid approach
is to improve the accuracy of the intrusion detection
system and to provide better result than other clustering.
3. Int. J. Advanced Networking and Applications
Volume: 07 Issue: 02 Pages: 2674-2681 (2015) ISSN: 0975-0290
2676
VenkataSuneethaTakkellapati [21] has proposed
a new system where Information Gain (IG) and Triangle
Area based KNN algorithm are used for selecting more
discriminative features. Then Greedy k-means clustering
algorithm is combined with SVM classifier to detect
Network attacks. This system achieves with high accuracy
detection rate and less error rate .All this work are carried
out in KDD CUP 1999 training data set.
Vaishali Kosamkar [20] has followed same
technique of combining C4.5 Decision Tree and Support
Vector Machine (SVM) algorithm in order to achieve high
accuracy and diminish the false alarm rate. In feature
selection stage, Correlation- Based Feature Selection
(CFS) algorithm is used for better accuracy result.
HarmeetKaurl [7] has designed their work to
reduce the delay in the network and to produce end to end
data in good speed. So in order to achieve, they have
simulated WSN using SPEED protocol. They have used
two performance parameters throughput and energy
consumption for analysis. BCO (Bee Colony
Optimization) algorithm is used to give better results with
high throughput and low energy consumption. All the
simulations are carried out in MATLAB.
3. METHODOLOGY
This research work aims at detecting DoS attack
in WSN using Hybrid technique. The objective is to
improve accuracy level and reduce misclassification and
false positive rate. The steps involved in this proposed
research design are shown in fig1.
Figure 1.Overview of Methodology
3.1 Pre-processing
It is the main step in improve the data quality.
Data from the dump area or from real-time environment
consists of noise, inconsistent, incomplete, missing value,
numeric and non-numeric data. Such type of data must be
cleaned using preprocessing techniques. Since in this
work both TCP dump and simulated dataset are used,
probability method is used to convert all non-numeric
values into numeric values in both datasets.
3.2 Normalization
Data normalization is a method to convert the
data vector into a new data vector where numeric values
fall within a specified range, such as scaling values
between [0,1]. This allows better comparisons or
visualizations of attributes that are of different units.
There are many types of normalization such as min-max,
z-score and decimal scaling normalization. The
normalization method used for this data is Min-Max
Method. It transforms all feature value to fall between
specified range [0, 1], since each value has different
ranges. The normalized value of ei for variable E in the ith
row is calculated as:
� � � ����� �� =
�� − � �
� �� − � �
Where,
Emin = the minimum value for variable E
Emax = the maximum value for variable E
If Emax is equal to Emin then Normalized (ei) is set to 0.5.
3.3 Feature Selection
Using Feature Selection technique it selects specific
subset of features to achieve the target output. The main
aim of feature selection is to remove the redundant and
irrelevant attributes (features), it is also named as attribute
subset selection [15].Through this selection, the level of
accuracy increases, with a reduction on dimensionality
and over fitting. Principal Component Analysis
approach monitors the variables and their relationship to
one another. It reduces the number of variables in
regression and clustering, for example. Each principal
component in Principal Component Analysis is the
linear combination of the variables which gives a
maximized variance. The steps in PCA are as follows:-
i) it assign scoring to each feature and based on the
scoring the features are either kept or removed from
dataset to achieve the target output and
ii) It finds a linear projection in high dimensional data
and converts them into lower dimensional subspace
that helps to minimize the reconstruction error.
Figure 2. Procedure for PCA
Subtract the mean from the dataset in all the
n-dimensions
Calculate the covariance matrix of this mean
subtracted dataset.
Calculated the eigenvalues and eigenvectors
of the covariance matrix found in step2.
Forming a feature vector by selecting the
eigenvector with the largest eigenvalues
Finally Results are produced.
Benchmark
Dataset
Simulated
Dataset
Preprocessing
Normalization
ACO
Feature
Selection
Performance
Evaluation
HMM+ACOHMM
4. Int. J. Advanced Networking and Applications
Volume: 07 Issue: 02 Pages: 2674-2681 (2015) ISSN: 0975-0290
2677
3.4 Techniques
3.4.1 Hidden Markov Model (HMM)
In this work, HMM perform a generative model
that can model data sequentially in nature. HMM is used
to model data by assuming Markov property .Suppose a
system with N states and at discrete time intervals
transition take place among states. Let these instances be
t, t = 1, 2, 3 ... .any process is said to be a Markovian, if
only if the conditional probability of future states, depend
only upon the present state. The identification and
equations of HMM consists of 5tuples i.e. [N,M,A,B,π]
[20].
Where,
N denotes the number of states Q = {Q1, Q2,.....Qn}.
M, number of observation symbols, V= {V1, V2 ... VM}.
Hidden Markov Model use two different algorithms which
perform different task, they are Baum- Welch and Forward
–Backward algorithm. Baum-Welch algorithm learns only
the parameter of the model {A, B, π} and Forward-
Backward algorithm learns the probability of occurrence of
an observation sequence from the given model, P|O|λ. Here
HMM algorithm learn the parameter, and compute the
probability of an output sequence on both dataset using
Forward-Backward technique.
Figure 3.Pseudocode for HMM
3.4.2 Ant Colony Optimization (ACO)
Ant colony optimization is a probabilistic
technique initially used for solving computational problem,
later it was used for finding good path through graph. ACO
algorithm belongs to the class of swarm intelligence
methods, it constitutes some novel optimizations. To
achieve the goal of finding an optimal path in a graph,
ACO algorithm follows the behavior of an ant in seeking
its food in its own colony. When an ant runs for food into
an object automatically it measures ‘colony similarity’
within its local range. This run decides whether to pick up
or drop the object according to the value of probability.
That finally diversified to solve a wider class of numerical
problems, and as a result, several problems have emerged,
drawing on various aspects of the behavior of ants.
Figure 4. Pseudo code for ACO
A definition of colony similarity is the similarity between
an un-clustering object and other objects within its local
range [9] .It possesses properties like flexibility,
robustness, decentralization, and self-organization; it can
suggest very interesting heuristics and it is used in both
dataset.
3.4.3 Hidden Markov Model combined with Ant
Colony Optimization (HMM+ACO)
Combination of techniques is the best way to
improve the overall performance in intrusion detecting
system .Here new hybrid algorithm is developed by
combining the two existing algorithm that is discussed
above (HMM and ACO).In this algorithm, both HMM
and ACO are two interactive phase which multiples the
Iterate the following stages until the termination
condition is met:
Forward stage
Initialize alpha0(start state) = 1, and alpha0(s) =
0 for all other states ‘s’.
Repeat for each i from 0 up to k-1:For each
state s: alphai+1(s) = sumall states s'alphai(s')*ps, s’*q
(yi+1 | s <-- s')
Backward stage
Initialize betak(s) = 1 for all states s
Repeat for each i from k down to 1:For each
state s: betai-1(s) = sumall states s'ps',s*q (yi | s' <--
s)*betai(s')
Initialization
for i=1 to I (I=cycle number)
If i=1 then generate m random ant within
range
else reduce FS within range [xt-1
best
+β; xt-1
best
-
β]
end if
for i =1 to m
Determine f (xt
best
)
Save xt
best
end
Pheromone Update
Pheromone evaporation
Update Pheromone trail
Solution phase
Determine search direction
Generate the values of α vector
for i= 1 to m
Determine the values of new colony
Determine new f ( xt
best
)
Save xt
best
end
If f(xt
best
)new
≤ (xt
best
)old
then xglobalmin
=
( xt
best
)new
else xglobalmin
=( xt
best
)old
end if
end
5. Int. J. Advanced Networking and Applications
Volume: 07 Issue: 02 Pages: 2674-2681 (2015) ISSN: 0975-0290
2678
iteration and executing time. HMM generates ‘pState’
value as output whereas ACO produce ‘GlobalMin’ value
as output .To enhance the result of HMM and ACO, both
values are hybridized to give a new optimization structure
and a better result.
Figure 5.Pseudocode for ACO
4. RESULTS AND DISSCUSSION
4.1. Experimental Setup
In this work, two different types of dataset i.e. i)
Benchmark Dataset and ii) Simulated Dataset are used
and performance is evaluated using MATLAB13b.
4.1.1 Benchmark Dataset
This experiment uses Intrusion Detection
Evaluation dataset which was first used in “The Third
International Knowledge Discovery and Data Mining
Tools Competition”. This dataset contains TCP dump
generated data over a nine week periods of simulated
network traffic in a hypothetical military LAN. It includes
7 million TCP connection records which have 21 types of
attacks in that only DoS attacks are considered such as
Back, Neptune, Pod, Smurf, Teardrop and normal attack
and it has 41 features which is categorized as follow:
Basic TCP features (1-9) are derived from packet
headers without inspecting the payload.
Time- and Host-Based Traffic features (10-28)
capture both present and historical data
Content features (29-41) are domain knowledge
which is used to assess the payload of the TCP
packets. i.e. no of failed login attempts.
4.1.2 Simulated Dataset
In this experiment, MATLAB software is used to
simulate a WSN based on LEACH protocol with and
without attack .Initially nodes are distributed randomly in
the network topology in a square area of 100m*100m.
Various parameters used for simulation are shown in
Table 1
Table. 1 Simulation Parameters
Simulated dataset are based on different
scenarios i.e. normal mode and attack mode. Initially in
normal mode, an event occurs in network and sensor
nodes transmit packets to the base station in each round of
simulation. In attack mode, once DoS attack is detected at
any node, the service passing through that particular node
automatically stops during simulation of ‘n’ no of nodes
where n is varied from 50-200 nodes. From the
experimental setup, the extracted features are extracted
and it is given as input for pre-processing and then
normalized using min-max normalization. Then principal
component analysis (PCA) is applied to reduce the
dimensionality of the normalized features. Finally a
hybrid technique is applied to classify it as normal or
abnormal and the performance is evaluated.
4.2 Performance Evaluation
The parameters used to evaluate the performance
of proposed approach are Accuracy, False Positive Rate
(FAR), and Misclassification Rate. They are defined as
follows,
1) Accuracy
The Accuracy is defined as the number of
intrusion instances detected divided by the total number of
intrusion instances present in the data set. The formula to
estimate the Accuracy is,
Accu�acy =
Numbe� of Int�u�ion� detected
Total Numbe� of Int�u�ion� P�e�ent
X
2) False Positive Rate (FPR)
False positive rate (FPR) is defined as the ratio of
the numbers of abnormal measurements that are
incorrectly misclassified as normal to the total number of
normal measurements.
Simulation Parameters Value
Field Dimensions(in meters) 100 *100m
Packet size 4000 bits
Number of Nodes 50,100,150,
200
Optimal Election Probability of
a node to become cluster
head(p)
0.1
Tx& Rx Antenna gain (Gt=Gr) 50j/energy
Tx& Rx Antenna heights (in m) 1
Percentage of attack node Upto 1%
Maximum number of rounds (r) 250
Input: A new data item x.
Input: GlobalMin (ACO) and pStates (HMM)
from Individual algorithm.
Output: L – the label of x.
Begin
LH ←performance of x with HMM;
LA ← performance of x with ACO;
if LH = LA = normal then
L ← normal;
else if LH<>LA then
L ← amphibious;
else
L ← LA;
(Generate new optimization iteration)
(A sub-class of abnormal is detected by the
HMM+ACO abnormal algorithm.)
end
end
6. Int. J. Advanced Networking and Applications
Volume: 07 Issue: 02 Pages: 2674-2681 (2015) ISSN: 0975-0290
2679
FPR =
Numbe� of mi�cla��ified abno�mal mea�u�ement�
Total Numbe� of no�mal mea�u�ement�
X
3) Misclassification Rate
It is defined as the degree of errors
encountered during data transmission over a
communications or network connection. It is also denoted
as “Error Rate”.
Mi�cla��ification Rate =
−
Numbe� of co��ected cla��ified connection�
Total Numbe� of connection�
X
The performance results are obtained and the
proposed method of HMM+ACO is compared with
HMM, ACO are tabulated in the following table. From the
tables it is observed that hybrid technique (HMM+ACO)
gives improved results when compared to HMM and
ACO in all metrics.
4.3 Results:
A sample results for 150 nodes with DOS
attacks is shown in the following figures 6, 7 and 8.
Table .2 Comparison of HMM, ACO and HMM+ACO on
Benchmark dataset
Benchmark Dataset
Metrics HMM ACO HMM+ACO
Accuracy 79.5 73.4 84.39
False Positive
Rate (FPR)
0.905 0.423 0.074
Misclassification
Rate
0.204 0.265 0.166
Table .3 Comparison of HMM, ACO and HMM+ACO on
Simulated dataset.
Simulated Dataset
Metrics HMM ACO HMM+ACO
Accuracy 89.55 82.66 93.83
False Positive
Rate (FPR)
0.100 0.452 0.077
Misclassification
Rate
0.104 0.173 0.061
Figure 6.Detection of Attacker Nodes using 150 nodes
Figure 7. DOS attack occur at 20th
node using 150 nodes
7. Int. J. Advanced Networking and Applications
Volume: 07 Issue: 02 Pages: 2674-2681 (2015) ISSN: 0975-0290
2680
Figure 8. Packets sent to Base Station using 150 nodes.
From the experimental results, it is concluded
that hybrid technique gives improved results than HMM
and ACO. Accuracy result has been increased almost 10%
in the case of both datasets. False Positive Rate and
Misclassification rate has decreased by 2% almost using
the both datasets.
5. CONCLUSION AND FUTURE
ENHNCEMENT
There are different intrusion detection
mechanisms available in the literature. There are many
intrusions that affect the network day to day life but DoS
is the most common attack that affects the environment.
Therefore, the work has focused on detecting DoS attack
using hybrid technique in WSN. The experimental results
showed that the proposed technique achieves better
Accuracy when compared to existing algorithms. The
proposed hybrid technique (HMM+ACO) results are
compared with earlier algorithms such as HMM and
ACO.
Following are identified as the scope for future
enhancement,
In the future, Candid-Covariance free Incremental
Principal Component Analysis (CCIPCA) can be
operated instead of Principal Component Analysis
(PCA) for dimensionality reduction .It can be used in
the incremental mode to simulate the real time
applications.
The simulated data set can be further analyzed for
classifying of other different type of attacks (i.e. Sink
attack, Hello attack etc) where proposed technique can
be used.
REFERENCES
[1] Amit Kumar Mishra, Sunil Ghildiyal, Ashish Gupta,
Neha Garg ,” Analysis Of Denial Of Service (Dos)
Attacks In Wireless Sensor Networks”, IJRET:
International Journal of Research in Engineering and
Technology, Volume: 03 Special Issue: 10 |
NCCOTII 2014 | Jun-2014.
[2] Animesh Patcha and Jung-Min Park, “An overview of
anomaly detection techniques: Existing solutions and
latest technological trends”, Elsevier Computer
Networks, Vol. 51, 2007.
[3] Dat Tran, Wanli Ma, and Dharmendra Sharma,”
Network Anomaly Detection using Fuzzy Gaussian
Mixture Models”, International Journal of Future
Generation Communication and Networking, pp.37-
42, 2012.
[4] Harmeet Kaur , Ravneet Kaur, “ Crossbreed Routing
Protocol for SPEED Terminology in Wireless Sensor
Networks”, International Journal of Advance
Research in Computer Science and management
Studies, Volume 2, Issue 7, ISSN: 2321-7782, July
2014.
[5] Hayoung Oh,” Attack Classification based on Data
Mining Technique and its application for Reliable
Medical Sensor Communication”, International
Journal of Computer Science and Applications, Vol.
6, No. 3, pp 20 – 32, 2009.
[6] Jue Lu, Rongqiang Hu, “A new hybrid clustering
algorithm based on K-means and ant colony
algorithm”, Proceedings of the 2nd International
Conference on Computer Science and Electronics
Engineering (ICCSEE 2013).
[7] Levent Koc , Thomas A. Mazzuchi, Shahram Sarkani,
“A network intrusion detection system based on a
Hidden Naïve Bayes multiclass classifier”, Elsevier,
pp.13492–13500, 2012.
[8] Megha Bandgar, Komal dhurve, Sneha Jadhav,Vicky
Kayastha,Prof. T.J Parvat, “ Intrusion Detection
System using Hidden Markov Model (HMM)”,
IOSR Journal of Computer Engineering (IOSR-
JCE) e-ISSN: 2278-0661, p- ISSN: 2278-
8727Volume 10, Issue 3, pp.66-70, (Mar. - Apr.
2013).
[9] Miao Xie, SongHan, BimingTian and, SaziaParvin,”
Anomaly detection in wireless sensor networks: A
survey”, Elsevier Journal of Network and
Computer Applications, Vol.34, pp. 1302-1325,
2011.
[10] Mohammad Wazid , “ Hybrid Anomaly Detection
using K-Means Clustering in Wireless Sensor
8. Int. J. Advanced Networking and Applications
Volume: 07 Issue: 02 Pages: 2674-2681 (2015) ISSN: 0975-0290
2681
Networks” , Center for Security, Theory and
Algorithmic Research, pp. 1-17.
[11] Mohit Malik, Namarta kapoor, Esh naryan, Aman
Preet Singh,” Rule Based Technique detecting
Security attack for Wireless Sensor network using
fuzzy logic”, International Journal of Advanced
Research in Computer Engineering & Technology
,Volume 1, Issue 4, , ISSN: 2278 – 1323, June
2012.
[12] Murad A.Rassam, Anazida Zainal and Mohd
Aizaini Maarof,”An Efficient distributed anomaly
detection model for wireless sensor networks”,
Elsevier AASRI Procedia, No.5, pp. 9-14, 2013.
[13] Punam Mulak, Nitin R. Talhar, “Novel Intrusion
Detection System Using Hybrid Approach”,
International Journal of Advanced Research in
Computer Science and Software Engineering,
Volume 4, Issue 11, ISSN: 2277 128X, November
2014.
[14] Reda M. Elbasiony , Elsayed A. Sallam , Tarek E.
Eltobely ,Mahmoud M. Fahmy ,” A hybrid
network intrusion detection framework based on
random forests and weighted k-means” Ain Shams
Engineering Journal”, vol 4, pp.753–762,2013.
[15] Revathi, T. S. (2013). "Survey: Effective Feature
Subset Selection Methods and Algorithms for High
Dimensional Data". International Journal of
Advanced Research in Computer Engineering &
Technology (IJARCET).
[16] Shi-Jinn Horng , Ming-Yang Su , Yuan-Hsin Chen ,
Tzong-Wann Kao, Rong-Jian Chen, Jui- Lin Lai ,
Citra Dwi Perkasa ,” A novel intrusion detection
system based on hierarchical clustering and
support vector machines” , Elsevier Computer
Network pp.306–313, 2010.
[17] Kumar Singh 1, M P Singh 2, and D K Singh, "A
Survey on Network Security and Attack Defense
Mechanism for Wireless Sensor Networks",
International Journal of Computer Trends and
Technology- May to June Issue 2011.
[18] Shun-Sheng Wang, Kuo-Qin Yan , Shu-Ching
Wang , Chia-Wei Liu ,” An Integrated Intrusion
Detection System for Cluster-based Wireless
Sensor Networks”, Elsevier, pp. 15234–15243,
2011.
[19] Vahid Golmah, “ An Efficient Hybrid Intrusion
Detection System based on C5.0 and SVM”,
International Journal of Database Theory and
Application Vol.7, No.2 ,pp.59-70, (2014).
[20] Vaishali Kosamkar, Sangita S Chaudhari,”
Improved Intrusion Detection System using
C4.5Decision Tree and Support Vector Machine”,
International Journal of Computer Science and
Information Technologies, Vol. 5 (2) , pp. 1463-
1467, 2014
[21] Venkata Suneetha Takkellapati1 , G.V.S.N.R.V
Prasad,” Network Intrusion Detection system based
on Feature Selection and Triangle area Support
Vector Machine”, International Journal of
Engineering Trends and Technology-
Volume3Issue4- 2012
[22] Wenying Fenga, Qinglei Zhangc, Gongzhu Hud,
Jimmy Xiangji Huange, “Mining network data for
intrusion detection through combining SVMs with
ant colony networks”, Elsevier , pp. 127-140, 2013
BIODATA OF THE AUTHORS
Ms.S.Sumitha Pandit has received her
MCA degree in 2014 from
Avinashilingam Institute for Home
Science and Higher Education for
Women, Coimbatore, TamilNadu, India.
She is now completed her M.Phil in 2015 in
Avinashilingam Institute for Home Science and Higher
Education for Women, Coimbatore, TamilNadu, India.
Her areas of interest are Wireless Sensor Networks and
Data Mining.
Dr.B.Kalpana Professor in Computer
Science, Avinashilingam University for
Women, Coimbatore, has around 25 years
of teaching and research experience. Her
areas of interest include Data mining and
Wireless sensor networks. She has served as a reviewer
for several journals in computer science. She has been
the Principal Investigator for a project funded by the
NRB in the area of wireless sensor networks. She has to
her credit research papers and book chapters in several
reputed national and international journals/books.