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.
The document proposes a security model for wireless sensor networks using zero knowledge protocol. It addresses security threats like cloning attacks, man-in-the-middle attacks, and replay attacks. The model uses a unique fingerprint for each node based on its neighboring nodes to detect cloning. It also uses zero knowledge protocol for sensor nodes to verify authenticity without transmitting cryptographic information, preventing man-in-the-middle and replay attacks. The paper analyzes the performance and security of the proposed model.
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.
TRUST FACTOR AND FUZZY-FIREFLY INTEGRATED PARTICLE SWARM OPTIMIZATION BASED I...IJCNCJournal
Mobile Ad hoc Networks (MANET) is one of the rapidly emanating technologies, which has gained attention in a wide range of applications in the fields of military, private sectors, commercials and natural calamities. Securing MANET is a dominant responsibility, and hence, a trust factor and fuzzy based intrusion detection and prevention system is proposed for routing in this paper. Based on the trust values of the nodes, the fuzzy system identifies the intruder, such that the path generated in the MANET is secured. Moreover, an optimization algorithm, entitled Fuzzy integrated Particle Swarm Optimization (FuzzyFPSO), is proposed by the concatenation of the Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) for the optimal path selection in order to provide secure routing. The simulation of the proposed methodology is NS2 simulator and analysis is carried out considering four cases, like without attack, flooding attacks, black hole attack and selective packet drop attack concerning throughput, delay and detection rate. The remarkable evaluation measures of the proposed Fuzzy-FPSO are the maximal throughput of 0.634, minimal delay of 0.044 , maximal detection rate of 0.697 and minimal routing overhead of 0.24550 And the evaluation measure for the case without any attacks are the maximal throughput of 0.762, minimal delay of 0.029 ,maximal detection rate of 0.805 and minimal routing overhead of 0.11511.
When talk about intrusion, then it is pre- assume
that the intrusion is happened or it is stopped by the intrusion
detection system. This is all done through the process of collection
of network traffic information at certain point of networks in the
digital system. In this way the IDS perform their job to secure the
network. There are two types of Intrusion Detection: First is
Misuse based detection and second one is Anomaly based detection.
The detection which uses data set of known predefined set of
attacks is called Misuse - Based IDSs and Anomaly based IDSs are
capable of detecting new attacks which are not known to previous
data set of attacks and is based on some new heuristic methods. In
our hybrid IDS for computer network security we use Min-Min
algorithm with neural network in hybrid method for improving
performance of higher level of IDS in network. Data releasing is
the problem for privacy point of view, so we first evaluate training
for error from neural network regression state, after that we can get
outer sniffer by using Min length from source, so that we
hybridized as with Min – Min in neural network in hybrid system
which we proposed in our research paper
EFFICACY OF ATTACK DETECTION CAPABILITY OF IDPS BASED ON ITS DEPLOYMENT IN WI...IJNSA Journal
Intrusion Detection and/or Prevention Systems (IDPS) represent an important line of defence against a variety of attacks that can compromise the security and proper functioning of an enterprise information system. Along with the widespread evolution of new emerging services, the quantity and impact of attacks have continuously increased, attackers continuously find vulnerabilities at various levels, from the network itself to operating system and applications, exploit them to crack system and services. Network defence and network monitoring has become an essential component of computer security to predict and prevent attacks. Unlike traditional Intrusion Detection System (IDS), Intrusion Detection and Prevention System (IDPS) have additional features to secure computer networks.
In this paper, we present a detailed study of how deployment of an IDPS plays a key role in its performance and the ability to detect and prevent known as well as unknown attacks. We categorize IDPS based on deployment as Network-based, host-based, and Perimeter-based and Hybrid. A detailed comparison is shown in this paper and finally we justify our proposed solution, which deploys agents at host-level to give better performance in terms of reduced rate of false positives and accurate detection and prevention.
INTRUSION DETECTION USING FEATURE SELECTION AND MACHINE LEARNING ALGORITHM WI...ijcsit
This document describes a proposed hybrid intrusion detection model that uses feature selection and machine learning algorithms with misuse detection. The model first selects important features from the NSL-KDD dataset and generates rules based on the behaviors of those features using J48 and CART algorithms. These rules are then used to build an intrusion detection framework that is tested on the NSL-KDD dataset, achieving an accuracy of 88.23%, outperforming other models that require prior learning of attacks. The proposed model works on the concept of misuse detection and can detect intrusions based on feature behaviors without any previous training.
Outstanding to the promotion of the Internet and local networks, interruption occasions to computer
systems are emerging. Intrusion detection systems are becoming progressively vital in retaining
appropriate network safety. IDS is a software or hardware device that deals with attacks by gathering
information from a numerous system and network sources, then evaluating signs of security complexities.
Enterprise networked systems are unsurprisingly unprotected to the growing threats posed by hackers as
well as malicious users inside to a network. IDS technology is one of the significant tools used now-a-days,
to counter such threat. In this research we have proposed framework by using advance feature selection
and dimensionality reduction technique we can reduce IDS data then applying Fuzzy ARTMAP classifier
we can find intrusions so that we get accurate results within less time. Feature selection, as an active
research area in decreasing dimensionality, eliminating unrelated data, developing learning correctness,
and improving result unambiguousness.
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
The nature of wireless networks itself created new vulnerabilities that in the classical wired networks do
not exist. This results in an evolutional requirement to implement new sophisticated security mechanism in
form of Intrusion Detection and Prevention Systems. This paper deals with security issues of small office
and home office wireless networks. The goal of our work is to design and evaluate wireless IDPS with use
of packet injection method. Decrease of attacker’s traffic by 95% was observed when compared to
attacker’s traffic without deployment of proposed IDPS system.
This document summarizes an article about intrusion detection systems (IDS) for secure mobile ad hoc networks (MANETs). It discusses the distributed and cooperative architecture of IDS for MANETs, where each node runs an IDS agent to detect intrusions locally and cooperate with other nodes. It describes several IDS approaches for MANETs including the Watchdog technique to detect misbehaving nodes, the Pathrater technique to find routes without those nodes, and the CORE technique which uses a collaborative reputation system. The document concludes that considering these IDS techniques can help make MANETs more secure.
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
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.
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.
This document summarizes a proposed network attack alerting system that aims to reduce redundant alerts from intrusion detection systems (IDS). The system uses both network-based and host-based IDS to detect attacks launched using the Backtrack penetration testing tool on a virtual network environment. Well-known open source IDS tools from the Security Onion distribution are used to generate alerts. The system builds a database of alerts and defines rules to eliminate duplicate alerts for the same attack based on attributes like source/destination IP and port. It also establishes a severity classification scheme using threshold values of alerts and time to help administrators prioritize responses.
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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.
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.
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.
This document summarizes a proposed network attack alerting system that aims to reduce the large number of alerts generated by intrusion detection systems (IDS). The system uses both network-based and host-based IDS to detect attacks launched using the Backtrack attacking tools on a virtual network lab environment. Well-known open source security tools on the Security Onion Linux distribution are used to generate alerts. The system defines rules to identify important alert types and stores alerts in a database. It aims to eliminate redundant alerts for the same attack by analyzing attributes like source/destination IP and port. Alert severity levels are defined using threshold counts and times to classify alerts and help administrators respond appropriately.
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.
A BAYESIAN CLASSIFICATION ON ASSET VULNERABILITY FOR REAL TIME REDUCTION OF F...IJNSA Journal
IT assets connected on internetwill encounter alien protocols and few parameters of protocol process are exposed as vulnerabilities. Intrusion Detection Systems (IDS) are installed to alerton suspicious traffic or activity. IDS issuesfalse positives alerts, if any behavior construe for partial attack pattern or the IDS lacks environment knowledge. Continuous monitoring of alerts to evolve whether, an alert is false positive or not is a major concern. In this paper we present design of an external module to IDS,to identify false positive alertsbased on anomaly based adaptive learning model. The novel feature of this design is that the system updates behavior profile of assets and environment with adaptive learning process.A mixture model is used for behavior modeling from reference data. The design of the detection and learning process are based on normal behavior and of environment. The anomaly alert identification algorithm isbuiltonSparse Markov Transducers (SMT) based probability.The total process is presented using real-time data. The Experimental results are validated and presentedwith reference to lab environment.
A Modular Approach To Intrusion Detection in Homogenous Wireless NetworkIOSR Journals
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.
- 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
AN IMPLEMENTATION OF INTRUSION DETECTION SYSTEM USING GENETIC ALGORITHMIJNSA Journal
Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. So Intrusion Detection Systems have
become a needful component in terms of computer and network security. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. So, the quest of betterment continues. In this progression, here we present an Intrusion
Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of network intrusions. Parameters and evolution processes for GA are discussed in details and implemented. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of our system we used the KDD99
benchmark dataset and obtained reasonable detection rate.
INTRUSION DETECTION SYSTEM USING CUSTOMIZED RULES FOR SNORTIJMIT JOURNAL
This document proposes an intrusion detection system using customized rules for the Snort tool to improve security. The system uses Wireshark to scan network traffic for anomalies, Snort to detect attacks using customized rulesets for faster response times, and Wazuh and Splunk to analyze log files. Rules are created using the Snorpy tool and added to Snort to monitor for specific attacks like ICMP ping impersonation and authentication attempts. When attacks are attempted, the system successfully detects them and logs the alerts. The integration of these tools provides low-cost intrusion detection capabilities with automated threat identification and faster response compared to existing Snort configurations.
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.
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.
Intrusion Detection against DDoS Attack in WiMAX Network by Artificial Immune...Editor IJCATR
IEEE 802.16, known as WiMax, is at the top of communication technology because it is gaining a great position in the wireless networks. In this paper, an intrusion detection system for DDOS attacks diagnosis is proposed, inspired by artificial immune system. Since the detection unit on all subscriber stations in the network is WIMAX, proposed system is a fully distributed system. A risk theory is used for antigens detection in attack time. The proposed system decreases the attack effects and increases network performance. Results of simulation show that the proposed system improves negative selection time, detection Precision, and ability to identify new attacks compared to the similar algorithm.
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
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.
Intrusion Detection Systems By Anamoly-Based Using Neural NetworkIOSR Journals
To improve network security different steps has been taken as size and importance of the network has
increases day by day. Then chances of a network attacks increases Network is mainly attacked by some
intrusions that are identified by network intrusion detection system. These intrusions are mainly present in data
packets and each packet has to scan for its detection. This paper works to develop a intrusion detection system
which utilizes the identity and signature of the intrusion for identifying different kinds of intrusions. As network
intrusion detection system need to be efficient enough that chance of false alarm generation should be less,
which means identifying as a intrusion but actually it is not an intrusion. Result obtained after analyzing this
system is quite good enough that nearly 90% of true alarms are generated. It detect intrusion for various
services like Dos, SSH, etc by neural network
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.
Evaluation of network intrusion detection using markov chainIJCI JOURNAL
Day today life internet threat has been increased significantly. There is a need to develop model in order to
maintain security of system. The most effective techniques are Intrusion Detection System (IDS).The
purpose of intrusion system through the security devices detect and deal with it. In this paper, a
mathematical approach is used effectively to predict and detect intrusion in the network. Here we discuss
about two algorithms ‘K-Means + Apriori’, a method which classify normal and abnormal activities in
computer network. In K-Means process, it partitions the training set into K-clusters using Euclidean
distance and introduce an outlier factor, then it build Apriori Algorithm to prune the data by removing
infrequent data in the database. Based on defined state the degree of incoming data is evaluated through
the experiment using sample DARPA2000 dataset, and achieves high detection performance in level of
attack in stages.
A Review Of Intrusion Detection System In Computer NetworkAudrey Britton
This document provides an overview of intrusion detection systems (IDS) and the techniques used to implement them. It discusses that IDS are used to detect malicious actions on computer networks and protect important files and documents. The document then summarizes that IDS have four main components - sensors to monitor the system, a database to store event information, an analysis module to detect potential threats, and a response module to address detected threats. It also categorizes IDS based on the data source, detection approach, structure, and how intrusions are detected. Finally, the document outlines various techniques used in IDS, including artificial intelligence methods like neural networks, fuzzy logic, genetic algorithms and machine learning approaches.
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.
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HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NETWORK
1. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.3, May 2013
DOI : 10.5121/ijnsa.2013.5305 45
HYBRID ARCHITECTURE FOR DISTRIBUTED
INTRUSION DETECTION SYSTEM IN WIRELESS
NETWORK
Seyedeh Yasaman Rashida
1Department of Computer Engineering, Shirgah Branch, Islamic Azad University
Shirgah, Mazandaran, Iran
s.y.rashida@gmail.com
ABSTRACT
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.
KEYWORD
Intrusion Detection, Agent, Architecture, Misuse detection, Signature-based
1. INTRODUCTION
With the evolution of computer networks, computer security has also revolted from securing
giant mainframes in the past to securing large scale unbounded computer networks. The need
for computer security has become even critical with the proliferation of information technology
in everyday life. The nature of threat has changed from physical infiltration and password
breaking to computer viruses, self-propagating and self-replicating worms, backdoor software,
Trojan horses, script kiddies, computer criminals, terrorists and the list is long.
The increase in dependability on computer systems and the corresponding risks and threats has
revolutionized computer security technologies. New concepts and paradigms are being adopted,
new tools are being invented and security conscious practices and policies are being
implemented. There is a clear need for novel mechanisms to deal with this new level of
complexity.
Network intrusion-detection systems (NIDSs) are considered an effective second line of defence
against network-based attacks directed to computer systems [4, 3], and – due to the increasing
severity and likelihood of such attacks – are employed in almost all large-scale IT
infrastructures [2]. Intrusion Detection System (IDS) must analyse and correlate a large volume
of data collected from different critical network access points. This task requires IDS to be able
to characterize distributed patterns and to detect situations where a sequence of intrusion events
occurs in multiple hosts. . 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.
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The rest of this paper is organized as follows: in Section 2 reviews literature of related works.
Section 3 presents an architecture of our hybrid IDS; While section 4 and 5 present unique
characteristics and disadvantages of our hybrid IDS. In section 6, we have evaluated the
performance of proposed scheme. And Section 7 and 8 concludes the paper with future research
directions and challenges in IDS.
2. RELATED WORKS
Generally, the deployment of WSN in an unattended environment and the use of wireless
signals as the media for communication make it easy for eavesdroppers to get the signals.
Moreover, the limitations in processing, storage and battery lifetime make the security issues of
these networks difficult. Different types of attacks against WSN have been explored in the
literature like, attacks on sensed data, selective forwarding attacks, sinkhole attacks, hello
flood attack and many more[5]. In the following we provide a review of some relevant prior
work. In [6], the mobile agent based intrusion detection system were developed which uses the
trace gray technique to detect the intrusions. A proposed efficient anomaly intrusion detection
system in Ad-hoc by mobile agents[7] which uses the data mining algorithm to detect
the attacks exploited by the intruders. Mobile agent based intrusion detection system for
MANET [9] proposed by yinan Li which uses the clustering and joint detection technique
to identify the intruders. In [21], Focus of the paper is on the clustering WSNs, designing
and deploying Cluster-based Intrusion Detection System (CIDS) on cluster-heads and
Wireless Sensor Network wide level Intrusion Detection System (WSNIDS) on the
central server. In [8], intrusion detection in distributed networks is studied. They consider
agent and data mining independently and their mutual benefits. M. Saiful Islam Mamun and
A.F.M. Sultanul Kabir propose a hierarchical architectural design based intrusion detection
system that fits the current demands and restrictions of wireless ad hoc sensor network.
In the proposed intrusion detection system architecture they followed clustering
mechanism to build a four level hierarchical network which enhances network scalability
to large geographical area and use both anomaly and misuse detection techniques for
intrusion detection. They introduce policy based detection mechanism as well as intrusion
response together with GSM cell concept for intrusion detection architecture [22]. The paper
[10] presents the preliminary architecture of a network level IDS. The proposed system
monitors information in network packets and learning normal patterns and announcing
anomalies. Another approach is presented in [13], in which Cooperative Security Managers
(CSM) are employed to perform distributed intrusion detection that does not need a hierarchical
organization or a central coordinator. Each CSM performs as local IDS for the host in which it
is running, but can additionally exchange information with other CSMs. The architecture also
allows for CSMs to take reactive actions when an intrusion is detected. Unclear aspects are the
mechanisms through which CSMs can be updated or reconfigured, and the intrusion detection
mechanisms that are used locally by each CSM.
The idea of employing widely distributed elements to perform intrusion detection, by emulating
to some extent the biological immune systems, and by giving the system a sense of “self”, has
also been explored [12].
Intrusion detection is the process of monitoring and analyzing the data and events occurring in a
computer and/or network system in order to detect attacks, vulnerabilities and other
security problems [16]. IDS can be classified according to data sources into host-based
detection and network-based detection. In host-based detection, data files and OS
processes of the host are directly monitored to determine exactly which host resources
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are the targets of a particular attack. In contrast, network-based detection systems monitor
network traffic data using a set of sensors attached to the network to capture any malicious
activities.
Networks security problems can vary widely and can affect different security requirements
including authentication, integrity, authorization, and availability. Intruders can cause
different types of attacks such as Denial of Services (DoS), scan, compromises, and worms and
viruses [17, 18]. The approach for using Agents in ID that was the foundation for our work was
proposed in [4, 3]. These papers introduced the idea of lightweight, independent entities
operating in concert for detecting anomalous activity, prior to most of the approaches mentioned
previously.
3. SYSTEM ARCHITECTURE
We propose new architecture for building IDSs that uses agents as their lowest-level element for
data collection and analysis and employs a structure to allow for scalability. In general, there are
mainly two techniques for intrusion detections: i) misuse (signature-based) detection and ii)
anomaly (behavior-based) detection [20]. In the paper, we apply both techniques. Purpose of
applying both techniques is in attempting to detect any attacks or intrusions in a system. As
shown in the Fig. 1, the proposed IDS architecture consists of seven modules – Tracker,
Anomaly Detection Module, Misuse Detection Module, Monitor, Signature Generator,
Inference Detection Module and Countermeasure Module combining the results of the three
detection modules.
Figure1. The proposed Architecture for Intrusion Detection System
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In the following sections, each module is explained in more detail.
a. Tracker: Tracker is an independently running entity that monitors certain aspects of a
host. The agent would then generate a report that is sent to the appropriate Monitor and also is
stored in Storage. The agent does not have the authority to directly generate an alarm. Usually,
Countermeasure Module will generate an alarm based on information received from one or
more agents/ detectors. By combining the reports from different agents, Monitor builds a picture
of the status of their host.
b. Monitor: Analyse an on-going process to find out whether it behaves according
expectation. On the other hand, the Monitor compares the received packets it observes with the
signatures or rules of normal patterns of behavior stored in Signature database by using pattern
matching algorithm. If Monitor finds any match then sends appropriate message for known
attack to the Misuse Detector Module. Also it enters entry in log file about the event that caused
the alert. If Monitor does not find any match then sends data to Anomaly detector for finding
anomaly using pattern mining technique.
c. Misuse Detector: The misuse detection agent is worked like Monitor but the difference
between them is on detail. In fact, each monitor acts as independent IDS and detects attacks for
itself only without sharing any information with another IDS node of the system, even does not
cooperate with other systems. So, all intrusion detection decisions are based on information
available to the individual node. Its effect is too limited. However, each node runs its own
misuse detector and finally they collaborate to form a global misuse detector. The agent is used
to analyse the data captured by the Monitor agent globally. It detects the known attacks in
network by using the pattern matching algorithm. If there is a similarity between the received
reports and attack signatures in the database, then it reports to Countermeasure Module for
deciding on solutions.
d. Anomaly Detector: The anomaly detection agent is used to detect the new or unknown
attacks by using the classification techniques. Classification is concerned with establishing the
correct class (or category) for an object. The classification is based on characteristics of the
object [15]. The anomaly detection agent collects the data from the monitor to analyse the data
to detect the unknown attacks. Then it classifies to detect the new attack. The specification of
the classification model is shown in Fig. 3. The first While loop generates the set of candidate
solutions. The second While loop prunes this set by obtaining new information. The method
finishes if one of the following three conditions is true.
Fig. 2 shows the corresponding inference structure. Three inferences are used in the method plus
a transfer function for obtaining the attribute value:
Generate candidate: In the simplest case, this step is just a look-up in the knowledge
base of the potential candidate solutions.
Specify attribute: There are several ways of realizing this inference. The simplest way
is to just do a random selection. This can work well, especially if the “cost” of obtaining
information is low. Often however, a more knowledge-intensive attribute specification is
required. One possibility is to define an explicit attribute ordering as is the case in a decision
tree. This requires domain knowledge of the form “if attribute a has value x then ask about
attribute b”. Often, experts can provide this type of attribute-ordering information. The
specification knowledge then takes the form of a decision tree. A more comprehensive approach
is to compute the attribute that has the highest information potential. Several algorithms for this
exist. This last approach can be very efficient but may lead to system behavior that is alien to
users and experts.
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Obtain feature: Usually, one should allow the user to enter an “unknown” value. Also,
sometimes there is domain knowledge that suggests that certain attributes should always be
obtained together.
Match: This inference is executed for every candidate, and produces a truth value
indicating whether the candidate class is consistent with the information collected so far. The
inference should be able to handle an “unknown” value for certain attributes. The normal
approach is that every candidate is consistent with an “unknown” value.
After classification, if anomaly detector finds any anomaly then send appropriate message to
Inference Module to more investigation. Otherwise, it sends the report to Countermeasure
Module to decide on solution and confront with the attack.
e. Inference Module: Inference is important component of Knowledge models. Inference
acts as the building blocks of reasoning process. In the inference knowledge we describe how
these static structures can be used to carry out a reasoning process. The module is the highest-
level entities in the architecture. They also have control and data processing roles that are
similar to those of the anomaly detectors. The main difference between inference module and
anomaly detector is that an inference module can control entities that are running in several
different hosts whereas anomaly detectors only control local agents. This part decides by
knowledge and rules in KB and Signature Base. A knowledge base contains instances of those
knowledge types which are related to user’s actions. This module uses the naïve bayes classifier
to detect the new attack. It classifies the data based on the dataset available in the knowledge
database. If the incoming data is detected as attack means then it reports to Signature Generator,
which in turn reports to alert agent about the attack. It updates the detected attack in the
database.
f. Signature generator: Signature generator creates rule or signature and makes new entry
in Signature database. Then it sends appropriate message to Monitor to reanalyse the attack.
g. The Signature database records enable the IDS to have a set of signature, criteria or
rules against which they can be used to compare packets as they pass through the host. The
signatures database needs to be installed along with the IDS software and hardware itself.
h. Countermeasure module: When Countermeasure module receives the alert message of
known attack from Detectors, it notifies the administrator in one of several ways that the
administrator has configured beforehand. The module might display a pop-up window or sends
an e-mail message to the designated individual, for example. Besides the automated response
sent to the administrator, this module can be configured to take action at the same time that an
alert message is received. Typical actions are: i) Alarm, in which an alarm is sent to the
administrator, ii) Drop, in which the packet is dropped without an error message being sent to
the originating computer; and iii) Reset, which instructs the IDS to stop and restart network
traffic and thus stop especially severe attacks. This module is also used by network
administrator to evaluate the alert message and to take proper actions such as dropping a packet
or closing a connection. The administrator can anticipate having to fine-tune the signature
database to account for situations that seem to the IDS to be intrusions but that are actually
legitimate traffic. For example, an adjustment might be made to enable traffic that might
otherwise be seen by the firewall as suspicious, such as a vulnerability scan performed by a
scanning device located at a particular IP address. The IDS could be configured to add a rule
that changes the action performed by the IDS in response to traffic from that IP address from
Alarm to Drop.
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Figure2. The classification Structure
Figure3. Method Control of classification Model
object
class
attribute
feature
truth
value
generate
specify
match
obtain
While new-solution generate (object→candidate) do
Candidate-classes:=candidate Union candidate-classes;
While new-solution specify(candidate-classes→attribute)
And length candidate-classes > 1 do
Obtain(attribute→new-feature);
Current-feature-set:=new-feature union current-feature-set
For each candidate in candidate-classes do
Match(candidate+current-feature-set→truth-value);
If truth-value = false then
Candidate-classes:= candidate-classes subtract candidate;
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4. ADVANTAGES OF THE PROPOSED ARCHITECTURE
The following are the list of unique characteristics of our IDS.
It will run constantly with minimal human supervision. It will create signatures of new
attacks.
It will be applied as Distributed IDSs.
It will be adaptive in nature and adapts the changes in user and system behavior.
It will provide information to tracking attackers.
Design of our IDS makes it fault tolerant, so that it will be able to recover from crashes.
It will be able to get its prior state and resume its operation without any adverse effect.
It will be able to monitor itself and detect attacks on it.
It will be accurate and thereby there will be less number of false positives and false
negatives.
5. DISADVANTAGES OF THE PROPOSED ARCHITECTURE
We have identified several shortcomings in the proposed architecture. Detection of intrusions at
the Inference Module is delayed until all the necessary information gets there from the agents.
This is a problem common to distributed IDSs. The architecture does not specify access control
mechanisms to allow for different users to have different levels of access to the IDS. This is an
issue that will need to be addressed.
In their control role, Inference Module is single points of failure. If an inference module stops
working, all the anomaly detector that it controls stop producing useful information. This can be
solved through a hierarchical structure where the failure of an Inference Module would be
noticed by higher level monitors, and measures would be taken to start a new inference and
examine the situation that caused the original one to fail. Another possibility is to establish
redundant monitors that look over the same set of anomaly detector so that if one of them fails,
the other can take over without interrupting its operation. If duplicated Inference modules are
used to provide redundancy, mechanisms have to be used to ensure that redundant inference
modules will keep the same information, will obtain the same results, and will not interfere with
the normal operation of the IDS.
6. IMPLEMENTATION
We have performed the analytical performance comparison of our proposed scheme with
existing schemes. We analysed their performance on two major factors i.e. Security and
Efficiency. The security factor is divided further into three parameters i.e. internal external and
novel threats. Internal threats are those attacks that are initiated or injected by the intruder
residing inside the network. External threats are from outside attackers. Novel threats are the
unusual or unrecognized form of the intrusions which have not occurred previously. Three types
of possible values used by these intrusions are low, high and medium that indicates how clearly
the proposed scheme identifies these intrusions.
We have given the low value to all those schemes that doesn’t provide defence against the
compromised node, under attack nodes, inside attackers, master or secret key is captured or the
node activity is dependent on the neighbourhood node information, trust relationship on nodes
etc. the medium value to the all those proposed scheme that identify the intrusion but does not
provide any defensive measurement how to handle them, generate false negative in large
amount. The high value to all those schemes that clearly identify the intrusion as well as provide
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the counter measure against that intrusion, compromise of one node will not make the whole
security of the system vulnerable.
We divide the efficiency factor into three parameters i.e. computation costs, network
bandwidth, node resource utilization and number of messages. Two types of values are used
high and medium in computation cost, network bandwidth and node resource utilization. We
have given high value to all those schemes that increases burden on network resource i.e.
cryptographic algorithms are resource hungry in nature that require extra computation and
memory overhead, communication steps between nodes increases, simultaneous transmission
increases the rate of collision that effect the bandwidth issues, large amount of false negative
dissipate the energy resources etc. The medium value is given to the scheme that uses victim
resources in order to discover an intrusion by using minimum network resources. The number of
messages which contains the integer value i.e. additional steps used by the proposed schemes in
order to identify the intrusion. Table 1 shows that our proposed scheme is efficient in several
aspects as compare to the existing schemes.
Table 1: Performance comparison between different existing schemes
7. FUTURE WORK
These are some of the specific points we have identified as relevant for future work:
o Developing agents.
o Communication mechanisms.
o Developing Inference Module.
o Semantics of the communication.
o Porting to other platforms.
o Deployment and testing.
o Global administration and configuration.
o Reliability and fault tolerance.
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8. CONCLUSION
The main characteristic of misuse (signature-based) intrusion detection technique is in
comparing incoming threats against a predefined knowledge base in order to decide whether the
threat is considered an attack or intrusion whilst anomaly detection technique involves looking
for any unexpected changes in behavior of a system against what is considered normal behavior.
Both misuse and anomaly detection techniques have their own advantages and disadvantages.
We have used features of both the intrusion detection techniques in our IDS Architecture. This
paper presents research from an ongoing study on the use of features of both the intrusion
detection techniques to design a novel and efficient hybrid IDS. The proposed design of IDS,
however, aims to be more accurate and it does not require more processing resources, thus
offering both speed and accuracy to detect the intrusions.
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