This document discusses a modular approach to intrusion detection in homogeneous wireless networks. It begins by introducing wireless networks and the need for intrusion detection systems (IDS) due to security vulnerabilities. It then discusses different types of IDS, including signature-based detection that identifies known attacks, and anomaly-based detection that identifies deviations from normal behavior but can result in high false positives. The document proposes a modular approach combining advantages of signature-based and anomaly-based detection for high detection rates and low false positives. Requirements for IDS in wireless networks are also outlined.
As the Supervisory Control and Data Acquisition (SCADA) system are deployed in infrastructures which are critical to the survival of a nation, they have emerged as a potential terrain for cyber-war, thus attracting the considered attention of ‘nation-states’. The analysis of worms like ‘stuxnet’ ‘flame’ and ‘duqu’ reveals the hand of a ‘nation-state’ in their design and deployment. Hence, the necessity to understand various issues in the defence of SCADA systems arises. The forensics of the SCADA system provide deep insight into the design and deployment of the worm (the malware) once the system is attacked. This is precisely the scope of this essay.
Report: Study and Implementation of Advance Intrusion Detection and Preventio...
This document discusses building an intrusion detection system that combines network-based and log-based detection. It proposes using the Security Onion distribution and its included tools like Snort, Sguil, Squert and OSSEC. It describes configuring Security Onion sensors to monitor network traffic and logs, storing alerts in databases, and using the management consoles to analyze alerts. The goal is to create a comprehensive security monitoring platform through centralized log management and correlation of network and host-based events.
Wireless Networks Security in Jordan: A Field Study
- The document summarizes a study that evaluated the security of wireless networks in Jordan through a process called "wardriving" where the researchers drove around with wireless network detection tools.
- The results found that the majority (79.52%) of wireless networks tested were unsecured and vulnerable. Most networks used either low levels of encryption (68.67%) or no encryption at all (11.45%).
- Nearly all networks broadcast the default SSID (92.17%), leaving them exposed to potential hackers since changing the SSID is a basic security precaution.
A Performance Analysis of Chasing Intruders by Implementing Mobile AgentsCSCJournals
This document summarizes a research paper that proposes using mobile agents to improve intrusion detection systems. The paper presents an architecture for an intrusion detection system that uses mobile agents to autonomously collect intrusion-related information from systems on a network. Information collector agents gather data, while chasing agents work to trace the path of intrusions and locate their origin. The paper evaluates this approach and discusses how mobile agents can enhance intrusion detection through their mobility and autonomous functionality.
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.
Detecting Anomaly IDS in Network using Bayesian NetworkIOSR Journals
In a hostile area of network, it is a severe challenge to protect sink, developing flexible and adaptive
security oriented approaches against malicious activities. Intrusion detection is the act of detecting, monitoring
unwanted activity and traffic on a network or a device, which violates security policy. This paper begins with a
review of the most well-known anomaly based intrusion detection techniques. AIDS is a system for detecting
computer intrusions, type of misuse that falls out of normal operation by monitoring system activity and
classifying it as either normal or anomalous .It is based on Machine Learning AIDS schemes model that allows
the attacks analyzed to be categorized and find probabilistic relationships among attacks using Bayesian
network.
As the Supervisory Control and Data Acquisition (SCADA) system are deployed in infrastructures which are critical to the survival of a nation, they have emerged as a potential terrain for cyber-war, thus attracting the considered attention of ‘nation-states’. The analysis of worms like ‘stuxnet’ ‘flame’ and ‘duqu’ reveals the hand of a ‘nation-state’ in their design and deployment. Hence, the necessity to understand various issues in the defence of SCADA systems arises. The forensics of the SCADA system provide deep insight into the design and deployment of the worm (the malware) once the system is attacked. This is precisely the scope of this essay.
Report: Study and Implementation of Advance Intrusion Detection and Preventio...Deepak Mishra
This document discusses building an intrusion detection system that combines network-based and log-based detection. It proposes using the Security Onion distribution and its included tools like Snort, Sguil, Squert and OSSEC. It describes configuring Security Onion sensors to monitor network traffic and logs, storing alerts in databases, and using the management consoles to analyze alerts. The goal is to create a comprehensive security monitoring platform through centralized log management and correlation of network and host-based events.
Wireless Networks Security in Jordan: A Field StudyIJNSA Journal
- The document summarizes a study that evaluated the security of wireless networks in Jordan through a process called "wardriving" where the researchers drove around with wireless network detection tools.
- The results found that the majority (79.52%) of wireless networks tested were unsecured and vulnerable. Most networks used either low levels of encryption (68.67%) or no encryption at all (11.45%).
- Nearly all networks broadcast the default SSID (92.17%), leaving them exposed to potential hackers since changing the SSID is a basic security precaution.
An Efficient Classification Mechanism For Network Intrusion Detection System Based on Data Mining
Techniques:A Survey..........................................................................................................................1
Subaira A. S. and Anitha P.
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Ogunlere Samson, Maitanmi Olusola and Gregory Onwodi
Intrusion Detection Techniques for Mobile Ad Hoc and Wireless Sensor Networks..............................1
Rakesh Sharma, V. A. Athavale and Pinki Sharma
Performance Evaluation of Sentiment Mining Classifiers on Balanced and Imbalanced Dataset ...........1
G.Vinodhini and R M. Chandrasekaran
Demosaicing and Super-resolution for Color Filter Array via Residual Image Reconstruction and Sparse
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Jie Yin, Guangling Sun and Xiaofei Zhou
Determining Weight of Known Evaluation Criteria in the Field of Mehr Housing using ANP Approach ..1
Saeed Safari, Mohammad Shojaee, Mohammad Tavakolian and Majid Assarian
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Survey on Host and Network Based Intrusion Detection SystemEswar Publications
With invent of new technologies and devices, Intrusion has become an area of concern because of security issues, in the ever growing area of cyber-attack. An intrusion detection system (IDS) is defined as a device or software application which monitors system or network activities for malicious activities or policy violations. It produces reports to a management station [1]. In this paper we are mainly focused on different IDS concepts based on Host and Network systems.
A Collaborative Intrusion Detection System for Cloud Computingijsrd.com
Cloud computing is a computing paradigm that shifts drastically from traditional computing architecture. Although this new computing paradigm brings many advantages like utility computing model but the design in not flawless and hence suffers from not only many known computer vulnerabilities but also introduces unique information confidentiality, integrity and availability risks as well due its inherent design paradigm. To provide secure and reliable services in cloud computing environment is an important issue. To counter a variety of attacks, especially large-scale coordinated attacks, a framework of Collaborative Intrusion Detection System (IDS) is proposed. The proposed system could reduce the impact of these kinds of attacks through providing timely notifications about new intrusions to Cloud users' systems. To provide such ability, IDSs in the cloud computing regions both correlate alerts from multiple elementary detectors and exchange knowledge of interconnected Clouds with each other.
This document discusses implementing an Intrusion Detection System (IDS) for WiFi security. The IDS would detect vulnerable activities of devices connected to the network and alert the system.
The paper provides background on common WiFi security vulnerabilities and attacks. It then describes the components and methodology of an IDS, including using sensors to monitor network traffic, analyzers to evaluate the traffic for attacks, and user interfaces to manage the system. The proposed IDS would collect network information using Wireshark, detect intrusions, and respond to threats to improve security for wireless networks.
This document discusses network intrusion detection systems (NIDS) and their ability to handle high-speed traffic. It introduces NIDS and their role in monitoring network traffic. The document presents an experiment that tests the open-source NIDS Snort under high-volume traffic. The experiment shows that Snort drops more packets as traffic speed and volume increases, demonstrating a weakness of NIDS in high-speed environments. It suggests using a parallel NIDS technique to help NIDS better handle high-speed network traffic and reduce packet dropping.
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.
IRJET- Detection and Localization of IDS Spoofing Attack in Wireless Sensor N...IRJET Journal
This document proposes using intrusion detection systems and k-means clustering to detect and localize spoofing attacks in wireless sensor networks used in vehicular networks (VANETs). VANETs have characteristics like highly dynamic topology and frequent link changes that make performance monitoring difficult. The proposed approach uses cluster heads acting as IDS to monitor packet transmissions within clusters and detect misbehaving nodes. When an attacker is detected, an alarm is passed to the source node to eliminate the attacker. Simulation results show the method can efficiently and robustly detect and locate spoofing attackers in VANET wireless sensor networks.
IoT Network Attack Detection using Supervised Machine LearningCSCJournals
The use of supervised learning algorithms to detect malicious traffic can be valuable in designing intrusion detection systems and ascertaining security risks. The Internet of things (IoT) refers to the billions of physical, electronic devices around the world that are often connected over the Internet. The growth of IoT systems comes at the risk of network attacks such as denial of service (DoS) and spoofing. In this research, we perform various supervised feature selection methods and employ three classifiers on IoT network data. The classifiers predict with high accuracy if the network traffic against the IoT device was malicious or benign. We compare the feature selection methods to arrive at the best that can be used for network intrusion prediction.
IMPROVED IDS USING LAYERED CRFS WITH LOGON RESTRICTIONS AND MOBILE ALERTS BAS...IJNSA Journal
With the ever increasing number and diverse type of attacks, including new and previously unseen attacks, the effectiveness of an Intrusion Detection System is very important. Hence there is high demand to reduce the threat level in networks to ensure the data and services offered by them to be more secure. In this paper we developed an effective test suite for improving the efficiency and accuracy of an intrusion detection system using the layered CRFs. We set up different types of checks at multiple levels in each layer. Our framework examines various attributes at every layer in order to effectively identify any breach of security. Once the attack is detected, it is intimated through mobile phone to the system administrator for safeguarding the server system. We established experimentally that the layered CRFs can thus be more effective in detecting intrusions when compared with the other previously known techniques.
Network infrastructures have played important part in most daily communications for business industries,
social networking, government sectors and etc. Despites the advantages that came from such
functionalities, security threats have become a daily struggle. One major security threat is hacking.
Consequently, security experts and researchers have suggested possible security solutions such as
Firewalls, Intrusion Detection Systems (IDS), Intrusion Detection and Prevention Systems (IDP) and
Honeynet. Yet, none of these solutions have proven their ability to completely address hacking. The reason
behind that, there is a few researches that examine the behavior of hackers. This paper formally and
practically examines in details the behavior of hackers and their targeted environments. Moreover, this
paper formally examines the properties of one essential pre-hacking step called scanning and highlights its
importance in developing hacking strategies. Also, it illustrates the properties of hacking that is common in
most hacking strategies to assist security experts and researchers towards minimizing the risk of hack.
IRJET- Local Security Enhancement and Intrusion Prevention in Android Dev...IRJET Journal
This document proposes a local security enhancement and intrusion prevention system for Android devices. It summarizes existing host-based intrusion detection systems and behavior-based intrusion prevention systems for Android smartphones. The proposed system uses net flow based clustering to identify anomalies and correlates with host-based features to detect malware intrusions. The goal is to provide versatile security for Android smartphones by detecting a wide range of attacks, including denial of service attacks and probing. The system aims to detect new attacks as well.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
NTRUSION D ETECTION S YSTEMS IN M OBILE A D H OC N ETWORKS : S TATE OF ...ijcsa
Mobile Ad Hoc Networks (MANETs) are more vulnerable
to different attacks. Prevention methods as
cryptographic techniques alone are not sufficient t
o make them secure; therefore, efficient intrusion
detection must be deployed and elaborated to facili
tate the identification of attacks. An Intrusion De
tection
System (IDS) aims to detect malicious and selfish n
odes in a network. The intrusion detection methods
used
normally for wired networks can no longer adequate
when adapted directly to a wireless ad-hoc network,
so existing techniques of intrusion detection have
to be changed and new techniques have to be determi
ned
to work efficiency and effectively in this new netw
ork architecture of MANETs. In this paper we give a
survey of different architectures and methods of in
trusion detection systems (IDSs) for MANETs
accordingly to the recent literature.
This document describes the design of an IIR filter using the LabVIEW graphical programming environment. It begins with an introduction to digital filters and IIR filters. It then discusses the different types of IIR filters including Butterworth, Chebyshev, inverse Chebyshev, and elliptic filters. The document presents the simulation of 4th order examples of each filter type using LabVIEW. It evaluates the performance and characteristics of each filter based on their frequency responses. The document concludes the IIR filter provides advantages over FIR filters for lower order designs and applications where linear phase response is not critical.
Investigation on Using Fractal Geometry for Classification of Partial Dischar...IOSR Journals
1) The document investigates using fractal geometry to classify partial discharge (PD) patterns from different insulation defects.
2) Fractal features like fractal dimension and lacunarity are extracted from 3D PD patterns using box counting. Each PD pattern has a unique fractal dimension and multiple lacunarity values depending on box size.
3) Neural networks are used to classify PD patterns based on their fractal features. The goal is to minimize input features to improve classification performance. Different lacunarity values from varying box sizes are analyzed to find those most useful for classification.
A Novel Approach for Tracking with Implicit Video Shot DetectionIOSR Journals
1) The document presents a novel approach that combines video shot detection and object tracking using a particle filter to create an efficient tracking algorithm with implicit shot detection.
2) It uses a robust pixel difference method for shot detection that is resistant to sudden illumination changes. It then applies a particle filter for tracking that uses color histograms and Bhattacharyya distance to track objects across frames.
3) The key innovation is that the tracking algorithm is only initiated after a shot change is detected, reducing computational costs by discarding unneeded frames and triggering tracking only when needed. This provides a more efficient solution for tracking large video datasets with minimal preprocessing.
Survey and Analysis of Medium Access Control Protocols for Wireless Sensor Ne...IOSR Journals
This document summarizes and compares four medium access control (MAC) protocols for wireless sensor networks: T-MAC, B-MAC, S-MAC, and DSMAC. It discusses the key constraints and characteristics of wireless sensor networks that impact MAC protocol design, including limited energy resources. For each protocol, it describes the protocol's operation, analyzes its power consumption and latency performance, and compares the protocols. It finds that T-MAC is more energy efficient than S-MAC due to its adaptive active period, but S-MAC has lower latency. DSMAC achieves lower power consumption than B-MAC through adaptive duty cycling.
The document discusses trends in online and mobile commerce. It notes that e-commerce has grown faster than non-e-commerce retail in recent years. Mobile commerce is also growing rapidly, with mobile devices accounting for over half of retail website traffic and nearly a quarter of online sales over Thanksgiving weekend. Retailers are increasingly investing in mobile optimization, omnichannel integration, personalization, and testing. B2B e-commerce is also growing significantly. The document advocates for creating reusable content that can be published across multiple channels to provide a consistent customer experience.
Survey of different Web Application Attacks & Its Preventive MeasuresIOSR Journals
This document summarizes different types of web application attacks and proposed preventative measures. It discusses denial of service (DOS) attacks, cross-site scripting (XSS) attacks, SQL injection attacks, and request encoding attacks that have occurred from 2012-2014. Statistics on the financial impact of these attacks on various industries are provided. The document then proposes solutions to prevent DOS attacks, XSS attacks, SQL injection attacks, and request encoding attacks. These include implementing input validation, output encoding, access control, and encryption. Overall, the document aims to survey common web application attacks and identify best practices for building secure applications.
- The document is a collection of essays, stories, poems and other writings by Christopher G. Burley that examine philosophical and social issues.
- It explores concepts like duality, free will, omniscience, and the relationship between the limited and unlimited.
- One story describes a magician who creates a woman but then struggles with loneliness, desire, and the friction between subject and object. God later explains to him the nature of duality and the human condition.
The document discusses methods for calculating the Debye temperature from experimental data for mixed Na x K 1−x Cl crystals grown from aqueous solution. It describes growing 14 crystals with different compositions and doping some with ZnS. The Debye temperature was calculated using four methods: 1) from melting point data, 2) from Debye-Waller factor measurements from X-ray diffraction, 3) from microhardness values, and 4) from the Kopp-Neumann relation. The results from the first three methods were compared to the Kopp-Neumann relation to assess the accuracy of the different calculation techniques.
LinkedIn is a professional social media network used to connect with colleagues and find jobs. It has over 187 million members, with 2 new members joining every second. Users create profiles to share their work history and connect with current and former coworkers, customers, and classmates. Recruiters and hiring managers use LinkedIn to search for potential candidates by viewing their profiles and connections. Maintaining a complete profile with recommendations and connections can help users be found by recruiters actively searching on LinkedIn.
Growth Performance of Rats Maintained On Citrullus colocynthis Seed Coat-base...IOSR Journals
This study evaluated the growth performance of rats fed diets containing Citrullus colocynthis seed coat, which is usually discarded as agricultural waste. Forty-nine rats were divided into seven groups and fed one of seven experimental diets for six weeks: a control diet containing 100% corn starch, or diets containing 100% or 50% inclusion of raw, boiled, or autoclaved Citrullus colocynthis seed coat. The results showed that diets with 50% boiled or autoclaved seed coat did not significantly affect feed intake, weight gain, or other growth parameters, but significantly reduced metabolizable energy. Diets with 100% raw, boiled, or autoclaved seed coat, or
This document describes a proposed high-speed linear feedback shift register (LFSR) design for a Bose-Chaudhuri-Hocquengham (BCH) encoder through the application of sample period reduction technique. Specifically:
1. The LFSR is used to generate parity bits that are concatenated with message bits to form a codeword for error detection and correction in the BCH encoder.
2. To increase throughput and speed, the LFSR is unfolded using parallel processing techniques like unfolding, which increases the number of message bits processed per clock cycle.
3. An unfolding factor is selected based on analyzing criteria like computational time and iteration bounds, to reduce the sampling period and thereby decrease
Quick Identification of Stego Signatures in Images Using Suspicion Value (Sp...IOSR Journals
This document summarizes a research paper that proposes a new algorithm for quickly identifying steganographic signatures in images. The algorithm calculates a "suspicion value" for each image based on its pixel aberration levels. Pixel aberration measures how much a pixel differs from its neighboring pixels. Higher aberration could indicate hidden data. The suspicion value is higher for images likely containing stego data and lower for innocent images. The algorithm aims to quickly filter images for more thorough analysis, helping address challenges of large image volumes and computation costs in steganalysis. It calculates suspicion values related to both "distributing" and "concentrating" stego algorithms to determine an overall suspicion value.
The document provides information about night photography techniques. It discusses planning night shoots by scouting locations during the day and considering lighting conditions. Moonlight, flashlights, and torchlights can be used to illuminate subjects. Long exposures require a tripod, cable release, low ISO, and painting subjects with light sources. Trial and error is needed to get correct exposures. Essential gear includes a camera with bulb mode, tripod, and light sources.
A Study On Recent Trends And Developments In Intrusion Detection SystemLindsey Sais
This document discusses recent trends and developments in intrusion detection systems. It covers several topics:
- Artificial intelligence and machine learning techniques like neural networks, genetic algorithms, and fuzzy logic can be applied to intrusion detection to identify patterns and anomalies.
- There are different types of intrusion detection systems, including network-based, host-based, and wireless intrusion detection. Signature-based and anomaly-based detection are also discussed.
- Popular open source intrusion detection tools like Snort are discussed as alternatives to commercial intrusion prevention systems for some organizations.
- Intrusion prevention systems not only detect intrusions but can also automatically block attacks in real-time.
NETWORK INTRUSION DETECTION AND COUNTERMEASURE SELECTION IN VIRTUAL NETWORK (...ijsptm
Intrusion in a network or a system is a problem today as the trend of successful network attacks continue to
rise. Intruders can explore vulnerabilities of a network system to gain access in order to deploy some virus
or malware such as Denial of Service (DOS) attack. In this work, a frequency-based Intrusion Detection
System (IDS) is proposed to detect DOS attack. The frequency data is extracted from the time-series data
created by the traffic flow using Discrete Fourier Transform (DFT). An algorithm is developed for
anomaly-based intrusion detection with fewer false alarms which further detect known and unknown attack
signature in a network. The frequency of the traffic data of the virus or malware would be inconsistent with
the frequency of the legitimate traffic data. A Centralized Traffic Analyzer Intrusion Detection System
called CTA-IDS is introduced to further detect inside attackers in a network. The strategy is effective in
detecting abnormal content in the traffic data during information passing from one node to another and
also detects known attack signature and unknown attack. This approach is tested by running the artificial
network intrusion data in simulated networks using the Network Simulator2 (NS2) software.
Network Intrusion Detection And Countermeasure Selection In Virtual Network (...ClaraZara1
Intrusion in a network or a system is a problem today as the trend of successful network attacks continue to rise. Intruders can explore vulnerabilities of a network system to gain access in order to deploy some virus or malware such as Denial of Service (DOS) attack. In this work, a frequency-based Intrusion Detection System (IDS) is proposed to detect DOS attack. The frequency data is extracted from the time-series data created by the traffic flow using Discrete Fourier Transform (DFT). An algorithm is developed for anomaly-based intrusion detection with fewer false alarms which further detect known and unknown attack signature in a network. The frequency of the traffic data of the virus or malware would be inconsistent with the frequency of the legitimate traffic data. A Centralized Traffic Analyzer Intrusion Detection System called CTA-IDS is introduced to further detect inside attackers in a network. The strategy is effective in detecting abnormal content in the traffic data during information passing from one node to another and also detects known attack signature and unknown attack. This approach is tested by running the artificial network intrusion data in simulated networks using the Network Simulator2 (NS2) software.
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.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Current Studies On Intrusion Detection System, Genetic Algorithm And Fuzzy Logicijdpsjournal
This document summarizes a research paper on current studies of intrusion detection systems using genetic algorithms and fuzzy logic. The paper presents an overview of intrusion detection systems, including different techniques like misuse detection and anomaly detection. It discusses using genetic algorithms to generate fuzzy rules to characterize normal and abnormal network behavior in order to reduce false alarms. The paper also outlines the dataset, genetic algorithm approach, and use of fuzzy logic that are proposed for the intrusion detection system.
- Wireless sensor networks are vulnerable to security attacks due to their distributed nature, multi-hop communication, and lack of resources. Intrusion detection systems play an important role in detecting attacks.
- There are three main types of intrusion detection systems: signature-based, anomaly-based, and specification-based (a hybrid of the two). Signature-based systems detect known attacks but miss new ones, while anomaly-based systems can detect new attacks but have high false positives.
- The paper compares these intrusion detection systems for wireless sensor networks and finds that anomaly-based systems have the lowest resource usage but may miss known attacks, while signature-based systems detect known attacks but use more resources. The best approach
Intrusion Detection System using AI and Machine Learning AlgorithmIRJET Journal
This document discusses using artificial intelligence and machine learning algorithms to develop an intrusion detection system (IDS). It begins with an abstract that outlines using AI to act as a virtual analyst to concurrently monitor network traffic and defend against threats. It then provides background on IDS and the need for more effective automated threat detection. The document discusses classifying attacks, different types of IDS (host-based and network-based), and detection methods like signature-based and anomaly-based. It aims to develop an IDS using machine learning algorithms that can learn patterns to provide automatic intrusion detection without extensive manual maintenance.
Optimized Intrusion Detection System using Deep Learning Algorithmijtsrd
A method and a system for the detection of an intrusion in a computer network compare the network traffic of the computer network at multiple different points in the network. In an uncompromised network the network traffic monitored at these two different points in the network should be identical. A network intrusion detection system is mostly place at strategic points in a network, so that it can monitor the traffic traveling to or from different devices on that network. The existing Software Defined Network SDN proposes the separation of forward and control planes by introducing a new independent plane called network controller. Machine learning is an artificial intelligence approach that focuses on acquiring knowledge from raw data and, based at least in part on the identified flow, selectively causing the packet, or a packet descriptor associated with the packet. The performance is evaluated using the network analysis metrics such as key generation delay, key sharing delay and the hash code generation time for both SDN and the proposed machine learning SDN. Prof P. Damodharan | K. Veena | Dr N. Suguna "Optimized Intrusion Detection System using Deep Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21447.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/21447/optimized-intrusion-detection-system-using-deep-learning-algorithm/prof-p-damodharan
This document describes a proposed artificial neural network based intrusion detection system. It uses a multilayer perceptron neural network architecture trained on the KDD Cup 99 intrusion detection dataset. The system monitors network traffic in real-time, extracts features from network packets, and classifies the traffic into six categories using the neural network. It is able to detect both known and unknown attacks. The system aims to improve upon traditional signature-based intrusion detection systems.
Intrusion detection systems aim to detect unauthorized access or activity in a computer system or network. There are two main types: network-based systems monitor network traffic to detect intrusions, while host-based systems monitor operating system logs and files on individual computers. Effective intrusion detection requires an incident response team to assess damage from intrusions and prevent future vulnerabilities, as well as securely storing logs as potential evidence.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document proposes a hybrid intrusion detection system (HIDS) for wireless sensor networks. The HIDS combines cluster-based and rule-based intrusion detection techniques. It is designed to address the limited resources of sensor networks while achieving high detection rates and low false positives. The system works by using cluster heads to detect intrusions based on both anomaly detection and comparing activities to known attack behaviors. A simulation evaluated the HIDS and found it performed intrusion detection efficiently while being energy efficient and having a high detection rate.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document proposes a signature-based intrusion detection system using multithreading. It captures network packets and analyzes them for intrusions by comparing signatures to databases of known attacks. A multithreaded design is suggested to improve performance by processing packets in parallel threads. Agents would be deployed on the network with detection modules that use caching of frequent signatures to speed up analysis. An update module would transfer new frequent signatures to the caches.
Network Forensics is scientifically proven technique to accumulate, perceive, identify, examine, associate, analyse and document digital evidence from multiple systems for the purpose of uncovering the fact of attacks and other problem incident as well as performing the action to recover from the attack. Many systems are proposed for designing the network forensic systems. In this paper we have prepared comparative analysis of various models based on different techniques.
This document summarizes an article that proposes integrating conditional random fields (CRFs) and a layered approach to improve intrusion detection systems. CRFs can effectively model relationships between different features to increase attack detection accuracy. A layered approach reduces computation time by eliminating communication overhead between layers and using a small set of features in each layer. The proposed system aims to achieve both high attack detection accuracy using CRFs and high efficiency using the layered approach. It presents integrating these two methods for intrusion detection to address issues with limited coverage, high false alarms, and inefficiency in existing systems.
Intrusion Detection & Prevention Systems (IDPS) are crucial for protecting computers and detecting threats in real time. As threats have grown in the 21st century, IDPS have also evolved, with different types providing various protection functions. Effective IDPS not only detect and prevent attacks, but also log events, create reports on recent attacks, and provide detailed information. Detection methods include signature-based detection by comparing traffic to known attacks, anomaly-based detection by identifying deviations from normal behavior, and policy-based detection by enforcing allowed functions.
A Review Of Intrusion Detection System In Computer NetworkAudrey Britton
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A Modular Approach To Intrusion Detection in Homogenous Wireless Network
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 14, Issue 6 (Sep. - Oct. 2013), PP 53-59
www.iosrjournals.org
www.iosrjournals.org 53 | Page
A Modular Approach To Intrusion Detection in Homogenous
Wireless Network
Ajita Mishra1
, Ashish Kumar Srivastava2
1
( PG Scholar, CSE, NIIST Bhopal, India)
2
( Associate Professor, CSE, NIIST Bhopal, India)
Abstract: Wireless network is the latest and popular technology nowadays. Due to its ample advantage in
various fields, it has always been the prime target for hackers and attackers to break through its security and
the number of attempts are growing day by day, so the need of real time monitoring and prevention system is
required. There are so many methods to detect and prevent intrusion in wireless network but they lack in so
many ways or are inefficient. This paper has an inclination to outline the fundamentals of intrusion detection in
wireless network, describing the form of attacks and state the motivation for intrusion detection in wireless
network and use the modular approach to detect intrusion in a wireless network. Then validation of proposed
approach on real network data with analytical model for intrusion detection which allows us to analytically
formulate intrusion detection possibility is done.
Keywords - WNS, IDS, attacks, SID, WIPS
I. Introduction
Interest in wireless network security has been growing in recent years. Although several security-
defense systems have been developed such as firewalls, encryption, authentication, and VPNs, most of the
wireless systems are still susceptible to attacks. Unfortunately, complete attack prevention in wireless networks
is not realistically attainable due to the openness of wireless medium, system complexity, configuration and
administration errors, abuse by authorized users, lack of centralized monitoring and management points,
dynamically changed network topologies, etc. The wireless network is a rapidly emerging new technology
square measure. New applications square measure being developed like in traffic, setting observance,
healthcare, military applications, home automation. A wireless network is susceptible to numerous attacks like
jam, battery avoidance, routing cycle, Sybil, cloning. To protect Wireless network against completely different
varieties of vulnerabilities, preventive mechanisms like cryptography and authentication will be applied to stop
some sorts of attacks. Additionally, these mechanisms squares the measures which are effective to guard from
outside attacks and didn't guarantee the interference of intruders from outside or within the network. Today
Intrusion detection is used as a security resolution in a much wired networks within the type of software/
hardware by that one will sight unwanted services happening in the system by the approach of
enhanced/abnormal network activity and determine suspicious patterns that will indicate whether or not the
network/system is beneath attack? For Wireless network many schemes were projected however they need
restricted options like a concern for attacks on a specific layer. A wireless IDS may aid within the detection of a
variety of attacks. In an attempt to spot potential WAP targets, hackers ordinarily use scanning computer code.
Hackers or curious people can use tools like Netstumbler or Kismat to plan a given area's WAPs.
Many types of wireless networks are used, they are following: Adhoc network [11] is a type
of wireless network and decentralized in nature. It is a set of wireless mobile nodes forming a momentary
network without any centralized access point. Decentralized nature of wireless ad hoc networks makes them
suitable for multiple applications, where central nodes can't be relied on and may develop the scalability of
networks as compared to wireless networks. Ad hoc network is also referred as IEEE 802.11 wireless networks.
The ad - hoc network goes through a lot of issues, congestion and security are the major issues of current
research, which leads to severe dilapidation of network throughput and increases the routing overheads. Mobile
ad hoc networks (MANET) are an application of Adhoc networks. Another wireless network is Sensor
Network. A wireless sensor network (WSN) [3] consists of spread autonomous sensors to monitor physical or
environmental conditions, such as sound, temperature, pressure, etc. Intrusion detection in Wireless Sensor
Network is of practical concern in many applications such as detecting an intruder in a battleground. Today such
networks are used in many consumer and industrial applications, such as machine health monitoring, industrial
process monitoring and control, traffic control resident application areas, environmental and local monitoring.
The rest of the paper is organized as follows. In the next section II we discussed about the IDS and classification
of the IDS system. Explaination of various types of exiting attacks and intrusion detection techniques in section
III. Our proposed work is discussed in section IV. Finally section V gives result and then section VI & VII
includes future work and conclusion.
2. A Modular Approach To Intrusion Detection in Homogenous Wireless Network
www.iosrjournals.org 54 | Page
II. Intrusion Detection System (IDS)
An Intrusion detection system (IDS) is hardware and/or software designed to sense superfluous
attempts at accessing, manipulating, and/or disabling of computer through a network, such as the Internet. These
attempts may take the form of attacks like crackers, malware and/or dissatisfied employees. IDS indirectly
detect attacks within properly encrypted traffic. An intrusion detection system is used to detect several types of
nasty behaviors that can give and take the security and trust of a computer system. This comprises network
attacks against data determined attacks on applications, host based attacks such as unauthorized logins and
access to sensitive files, privilege escalation, susceptible services, and viruses.
Refers to the collaboration degree of IDS agents on the monitored system. Based on the IDS architecture, we
differentiate between autonomous and distributed IDS systems.
1) Autonomous IDS: In autonomous IDS architecture, each network node operates independently and is
responsible for detecting attacks on its own accord; there is no interaction between the network nodes. This
architecture is more proper for the flat networks than for the multi-layered networks.
2) Distributed IDS: It comprises a number of network nodes which are responsible for collecting local audit data
independently, and then collaboratively investigate it in a broader range in order to carry out a global IDS. This
architecture is applicable for flat networks, and also for multilayered networks.
2.1 Detection Techniques
Detection techniques describe the detection manner. There are two complementary and one
hybrid detection techniques to differentiate between the normal and malicious traffic.
1) Signature-based detection technique: It is also known as Misuse Detection. In this technique, IDS system
inspects the monitored packets on the basis of detecting any facts of the attacks, according to a predefined and
created model for specific known attacks. The advantage of this detection technique is its capability to detect
instances of known attacks. The main disadvantage is the difficulty of gathering information about all current
attacks, and thus this leads to the lack of the ability to detect the newly invented attacks as well as some
variations of existing attacks, causing false negative.
Intrusion signatures have been characterized as a string, event sequences, graphs, and intrusion
scenarios (consisting of target states, event sequences, and their preconditions). FSM (finite-state-machine),
colored Petri Nets, associate roles and production rules of expert systems have been used to represent and
recognize intrusion signatures. Intrusion signatures are either physically encoded or manually learned through
data mining. But, signature recognition techniques have a limitation in that they cannot detect original intrusions
whose signatures are unknown.
2) Anomaly-based detection technique: In this technique, the IDS system inspects the system activities on the
basis of detecting any deviations from an established model of the normal and expected behavior through the
system. This technique does not require a prior knowledge of attacks, and thus it can detect the new attacks. The
main disadvantages are the disability to identify the attack type and the high false positive rate.
3) Specification-based detection technique: Combines the Advantages of signature-based detection and
anomaly-based detection techniques. This arrangement detects unknown attacks, using the detection rate of
anomaly detection, and accuracy of misuse detection. Consequently, it gets the targets of high detection rate and
low false positive rate.
2.2 Requirements of IDS in Wireless Network
Any IDS should discover a considerable share of intrusions into the supervised system, whereas keeping
the warning rate at a suitable level at a lower cost. It's expected that a perfect IDS is likely to support many of
the subsequent needs :
The IDS must not introduce a brand new weakness infrastructure. In the painter. That is, the IDS itself ought
to not build a node weaker than it already is.
Associate in Nursing IDS ought to run ceaselessly and stay transparent to the system and users.
The IDS ought to use very little system resources as potential to observe and stop intrusions. IDS that needs
excessive communication among nodes or run advance algorithms square measure not fascinating.
It should be fault-tolerant with in the sense that it must be ready to pass though system crashes, hopefully
recover to the previous state, and resume the operations before the crash.
Excluding sleuthing and responding to intrusions, associate in Nursing IDS ought to conjointly resist
subversion. It should monitor itself and observe if it's been compromised by the associate in Nursing
offender.
Associate in Nursing IDS ought to have a correct response. In other words, Associate in Nursing IDS must
3. A Modular Approach To Intrusion Detection in Homogenous Wireless Network
www.iosrjournals.org 55 | Page
not solely observe but conjointly answer detected intrusions, preferably while not human intervention.
Accuracy of the IDS is another major consider MANETs. Fewer false positives and false negatives square
measure desired.
2.3 Wireless Intrusion Prevention System
Wireless Intrusion Prevention System (WIPS) is a network device that monitors the radio spectrum for the
existence of un-authorized access points , and can do automatic intrusion prevention. The main purpose of a
WIPS is to prevent un-authorized network access to local area networks and other information resources by
wireless devices. WIPS which is an extension of WIDS not only detects wireless intrusions, but also can prevent
them.
2.4 Classification of Some Open-Source and Commercial Widss
III. Literature Review
There are various techniques implemented in the security of the wireless network and attacks that
affect the security of wireless system, so researchers have proposed some of techniques to introduce the basics
of the intrusion detection in Wireless network, the definition of the intrusion, kinds of intrusions/attacks in
Wireless network, the motivation and want for intrusion detection and therefore the challenges of developing an
honest intrusion detection theme for wireless network. The definition of the Intrusion/Attack: [4] defines the
intrusion as any set of actions that try to compromise the most parts of the safety system: the integrity,
confidentiality or handiness of a resource. Within the same work, the interloper so was outlined as a personal or
a cluster of people who take the action within the intrusion. [5] Adds the statement of success or failures of
those actions thus it additionally refers to the attacks against the PC system. Within the theme of wireless
detector network, the conception is still constant since the intrusion additionally targets any of the parts
mentioned above. The character of Wireless network and its special characteristics just like the harsh readiest,
energy constraints and therefore the media of communication makes them terribly liable to the intrusions quite
different networks. Following attacks occur in wireless network: [1]
Probing & Network Discovery:-
Before an attacker is capable to attempt any kind of wireless harm one of the main activities would be
for him to recognize the various wireless targets in range. This type of attack is described amongst the first
activities engaged by any attacker. There are two types of probing- active and passive probing. Active probing
involves the attacker actively sending probe needs with no SSID configured in order to request a probe reaction
with SSID information and other information from any access points in range. Active probing cannot detect for
access points that are covered or out of range of the attacker’s wireless transmission range. When an attacker
engages in passive probing, he/she is listening on all channels for all wireless packets being received and sent
without sending even a single packet, thus the detection capacity is not limited by its transmission power. A
superior example of a tool that uses active probing is NetStumbler and for passive probing, Wirehsark tool is
used .
Surveillance:-
Once the wireless aim has been recognized, the attacker can continue to gather information about the
network using tools like airodump or kismet. The gathered data can be saved in pcap format for following
offline analysis. If the traffic stream is not encrypted, directly the attacker could look at the traffic stream and
recognize the network parameters (e.g. IP address range, gateway, MAC address, etc.) from the traffic. If the
WIDS Detection
Techniques
Information
Source
Architecture Respons
e
SnortWirel
ess
Signature-based NIDS Autonomous/
Distributed
Passive/ Active
WIDZ Signature-based NIDS Distributed Passive/ Active
AirMangne
t
Signature-based
/Anomaly-based
NIDS Autonomous/
Distributed
Passive/ Active
AirDefence Signature-based
/Anomaly-based
NIDS Distributed Active
AirIDS Signature-based
/Anomaly-based
NIDS Distributed Active
Kismet Signature-based NIDS Autonomous Passive
4. A Modular Approach To Intrusion Detection in Homogenous Wireless Network
www.iosrjournals.org 56 | Page
traffic stream is WEP encrypted, there are WEP crackers which are available for him. Airodump is used to
collect all the encrypted packets and aircrack is then used to crack the WEP key given if enough WEP are
gathered.
DOS (Denial of Services) attack:-
Denial Of Service (DOS) attack make an attempt to prevent legitimate users from accessing some
services, which they are eligible for. For instance, an unauthorized user might send too many login requests to a
server using random user ids one after the other in quick succession, so as to flood the network and deny other
legitimate users from using the network facilities.
Impersonation:-
Another category of attacks that can be simply executed in a wireless network is the impersonation
attack. In such an attack, the attacker adjusts his MAC address to a MAC address which he found prior during
the surveillance state. This MAC address would most positively belong to an authorized client in the network.
This is generally done to overcome the MAC filtering abilities of access points where only a list of authorized
MAC addresses is allowed to use the wireless network. To adjust the MAC address manually in the windows,
locate the registry settings for your wireless NIC and add a new string call network address with the new MAC
address information to it.
Key fingerprint = AF19 FA27 2F94 998D FDB5 EE3D F8B5 06E4 A169 4E46
Man in the middle and Rouge AP:-
In this type of attack, the attacker attempts to introduce himself in the middle of a communication for
purposes of catching client’s data and could potentially adjust them by discarding them or sending them out to
the real target.
Man-in-the-middle attack is also known as:
Bucket-brigade attack
Fire brigade attack
Monkey-in-the-middle attack
In order to insert oneself in the middle of the communication, one has to achieve two tasks, first, the suitable AP
allocates the client must first be brought down or made “extremely hard” so as to create a “complex to connect”
scenario for the wireless client. Secondly, the attacker must set up an interchange rouge AP with the same
records as the original, for purposes of allowing the client to connect to it. With the fast development of wireless
network, the problems on wireless security have become more and more prominent. And the technologies of
firewall and intrusion detection cannot solve these problems satisfactorily. However, wireless intrusion
prevention systems which can prevent attacks for WLAN excellently have become the research hotspot. We
propose a common wireless intrusion prevention framework (CWIPF), and describe some key technologies used
in this framework. Finally, we proposed some study issues should be focused on in the future. Index Terms-
intrusion prevention, wireless LAN, CWIPF, network security [2].
The increasing confidence upon wireless networks has put tremendous emphasis on wireless network
security. Intrusion detection in wireless network has become an essential component of any helpful wireless
network security system, and has recently gained attention in both research and industrial communities due to
widespread use of wireless local area network (WLAN). Although some intrusion prevention systems have
recently appeared in the market, their intrusion detection capabilities are limited. This paper focus on detecting
intrusion or anomalous behavior of nodes in WLAN’s Using a modular technique. We explore the security
vulnerabilities of 802.11, numerous intrusion detection techniques, and different network traffic metrics also
called as features. Based on the study of metrics, proposed a modular based intrusion detection approach. [3]
Intrusion detection in Wireless Sensor Network (WSN) is of useful attention in various applications
such as detecting an intruder in a battlefield. The intrusion detection is a mechanism for a WSN to detect the
existence of improper, inaccurate, or anomalous moving attackers. In this paper, we have considered the issue
according to heterogeneous WSN models. Furthermore, we have considered two sensing detection models:
single-sensing detection and multiple-sensing detection. [4]
[19] In this paper, a novel framework to detect wireless network attacks based on anomaly analysis of
the behavior of wireless networks and data mining techniques. WSPS approach is based on multi-channel online
monitoring and anomaly analysis of device localization, frame behavior, and network access violations with
respect to multiple-observation time windows. Using wireless network resources, WSPS produces network
features, wireless-network-state machine violations, and generates wireless network flows (WNetFlows) for
multiple time windows, and utilize the dynamically renewed anomaly and misuse rules to identify complex
known and unknown wireless attacks and take appropriate proactive actions.
5. A Modular Approach To Intrusion Detection in Homogenous Wireless Network
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[20] In this paper, propose a separation table to detect intrusion in hierarchical wireless sensor networks and to
approximate the effect of intrusion detection effectively. The primary experiment shows that the isolation table
can detect and prevent intrusion’s attacks effectively. But this method is not good enough to detect anomaly
using IDS.
IV. Proposed Methodology
In our approach, cluster the wireless traffic data and then use the heuristic function to make each
instance intrusive or normal. The heuristic function is used in the execution of modules for individual features in
intrusion detection system. In which we search for the specific features collectively defined an activity (i.e.
Pattern) followed by an attack. Then we put these results of features in a table consist list of features with
respect to MAC or IP address of a node (i.e. We maintain a check list for individual node), so we can calculate
the intrusive behavior of a node rather than a particular attack. A technique adopted for the detection of features
is tabular in which create a list of features vertically and on the basis of detecting features the alarm can be
generated for the respective attacks. It is a reverse approach than the usual Intrusion Detection Systems in which
they detect specific attacks. In the earlier IDS, two checks were needed for the same feature in two different
attacks but in proposed Modular Approach there is only a single check required to detect same feature in both
attacks. The following steps are followed to implement modular approach for intrusion detection in wireless
environment:
Generate algorithm to implement modular approach.
Collecting knowledge of signature of attacks used in wireless networks.
Capture database of wireless network.
Implement approach in system compatible platform.
Algorithm for Intrusion Detection & Prevention Flow Chart
A Novel Solution for Intrusion Detection
& Prevention.
1. Begin
2. Sniff for 802.11 frames.
3. Save data in a file that can be accessed through
the system and in the required format.
4. Open file contains data of the network
4.1. Change hexadecimal code in decimal
format
4.2. Purse frames and extract MAC headers
from the frames
4.3. Check 802.11 frame types.
4.4. The extract feature requires to detect
intrusion
4.5. Search for the predefined signature of
attacks in the database
5. Log packet content
6. Send out an alarm if intrusion found (i.e.
Signature match)
7. Analysis data packet with isolated from
(Analysis illegal behaviors).
8. Save all the intrusion data in the event database.
9. Set working Frequency of monitoring channel.
10. Exit and Repeat
6. A Modular Approach To Intrusion Detection in Homogenous Wireless Network
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As discussed above SID and AID both have problems of higher false alarm rate due to inappropriate threshold
value to generate alarms for intrusion. An approach is needed that uses the combination of both of these
techniques. In modular approach we are using the signature based detection approach by detecting the feature
listed for known attacks as well as for checking the abnormal behavior through a table of feature detected so we
can detect the unknown attacks. During algorithm design we take care of the following basic terminologies of
modular approach that can detect known as well as unknown attacks. Because that system is designed to
implement for wireless network one more thing that is crucial to remember is that it should not increase the CPU
overhead more than 5-6%. In this method Intrusion detection system starts with 802.11 Frame thereafter it look
for format (Given format) to decide whether a particular file is intruders file or not. For that to any given data
change Hexadecimal to decimal format thereafter extract the MAC header from the frame then compare with
802.11 Frame type and extract intrusion detection from features with given predefined signature attacks. Then
with this required log file is open with shows the no of intruders in this file. Send out the alarm if intrusion is
detected such process is applied to every upload file.
V. Result
We have design and implement the CWIPF with Snort-wireless. We have created evaluating
experiments, based on the performance of CWIPF with Snort-Wireless. In our experiment,twenty wireless
threats square measures launched against the WLAN, together with DoS, MAC spoofing, MITM, rogue AP,
misconfigured AP attacks.When CWIPF is applied on 20 threats file then it detect 19 threats file which can
prevent attackers from damaging wireless networks. Snort-Wireless prevents only 5 threats out of detected 12
threats. Moreover, Snort-Wireless extracted three false alarms.The figure shows the efficiency of these two
WIPS at detecting and preventing wireless threats.
Number of Wireless Threats and Time in sec an Analytical Model.
7. A Modular Approach To Intrusion Detection in Homogenous Wireless Network
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VI. Future Work
In this research wireless intrusion detection and preventionthe algorithm has been developed which
shows to be effective in detection and prevention of intrusions. The intrusion detections are applied in internet
application and parallel computer interconnection network. The Algorithm can be extended and compare with
real time work such as WEKA tools.
VII. Conclusion
This paper examines the intrusion detection problem by characterizing intrusion detection possibility
with respect to the intrusion distance and the network parameters like sensing range, node density and
transmission range. The analytical model for intrusion detection allows us to analytically formulate intrusion
detection possibility within an assured intrusion distance under various application scenarios and then validate
our approach on real network data in which a a database of 20 files is used and then successfully detect the
signature that are provided in our experiment. Our result shows the name of application and port of the system
using that application, so it can be possible to punish that system if the system is designed to do that.
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