An Agent Based Intrusion Detection System for Wireless Network with Artificial Immune System (AIS) and Negative Clone Selection

Kumar, G. V. Pradeep; Reddy, D. Narasimha

doi:10.1109/icesc.2014.73

Cited by 14 publications

(6 citation statements)

References 8 publications

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“…Two popular approaches for distributing the computation of intrusion detection among the network nodes are biologically inspired techniques and trust-based techniques. Biologically inspired IDS try to create a complex global response to an attack from simple local interactions of network nodes, as in swarm intelligence-based techniques [17][18][19], and artificial immune systems-based approaches [20,21]. Trust-based techniques tend to have good attack-detection performance at the expense of increasing the bandwidth due to the required information exchange (e.g., trust metrics) among neighboring nodes [22][23][24].…”

Section: Network Intrusion Detection Systems For Rwnmentioning

confidence: 99%

LC-IDS: Loci-Constellation-Based Intrusion Detection for Reconfigurable Wireless Networks

Zuñiga-Mejia

Villalpando-Hernández

Vargas-Rosales

et al. 2021

Electronics

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Detection accuracy of current machine-learning approaches to intrusion detection depends heavily on feature engineering and dimensionality-reduction techniques (e.g., variational autoencoder) applied to large datasets. For many use cases, a tradeoff between detection performance and resource requirements must be considered. In this paper, we propose Loci-Constellation-based Intrusion Detection System (LC-IDS), a general framework for network intrusion detection (detection of already known and previously unknown routing attacks) for reconfigurable wireless networks (e.g., vehicular ad hoc networks, unmanned aerial vehicle networks). We introduce the concept of ‘attack-constellation’, which allows us to represent all the relevant information for intrusion detection (misuse detection and anomaly detection) on a latent 2-dimensional space that arises naturally by considering the temporal structure of the input data. The attack/anomaly-detection performance of LC-IDS is analyzed through simulations in a wide range of network conditions. We show that for all the analyzed network scenarios, we can detect known attacks, with a good detection accuracy, and anomalies with low false positive rates. We show the flexibility and scalability of LC-IDS that allow us to consider a dynamic number of neighboring nodes and routing attacks in the ‘attack-constellation’ in a distributed fashion and with low computational requirements.

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Section: Network Intrusion Detection Systems For Rwnmentioning

confidence: 99%

LC-IDS: Loci-Constellation-Based Intrusion Detection for Reconfigurable Wireless Networks

Zuñiga-Mejia

Villalpando-Hernández

Vargas-Rosales

et al. 2021

Electronics

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“…Kumar and Reddy [6] identified that wireless networks had intrusions both at packet and signal levels and it could be simple and too complex unlike IP networks. Conventional techniques may be unsuccessful in wireless network due to the complexity of identifying intrusions at different levels and variation in credentials at different nodes.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism

Rizwan

Khan

Abbas

et al. 2015

International Journal of Distributed Sensor Networks

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During the past few years, we have seen a tremendous increase in various kinds of anomalies in Wireless Sensor Network (WSN) communication. Recently, researchers have shown a lot of interest in applying biologically inspired systems for solving network intrusion detection problems. Several solutions have been proposed using Artificial Immune System (AIS), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and so forth. In this paper, we propose a bioinspired solution using Negative Selection Algorithm (NSA) of the AIS for anomalies detection in WSNs. For this purpose, we implement the enhanced NSA and make a detector set that holds anomalous packets only. Then the random packets are tested and matched with the detector set and anomalies are identified. Anomalous data packets are used for further processing to identify specific anomalies. In this way, the number of wormholes, packets delayed, and packets dropped are calculated and identified. Simulations are performed on a large dataset and the results show high accuracy of the proposed algorithm in detecting anomalies. The proposed NSA is also compared with Clonal Selection Algorithm (CSA) for the same dataset. The results show significant improvement of the proposed NSA over CSA in most of the cases.

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“…Conventional techniques which are used for intrusion detection fail in wireless networks. Hence, an agent based technique is proposed for gathering information from various nodes with an evolutionary artificial immune system for the detection of intrusion and preventing the same over the intrusive paths [6].…”

Section: Related Work Intrusion Detectionmentioning

confidence: 99%

A machine learning framework of neural network combined with PLS regression and artificial immune system for intrusion detection

Kumar¹,

Reddy²

2014

International Journal of Advanced Research in Computer and Comm

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An intrusion is essentially an attack on security layer in a TCP/IP protocol stack. Such attacks have definite signature for instance specific ports or RTT. Hence such attacks can be detected by cross verifying current communication signature with attack signatures. However attack signatures are evolutionary. Therefore using a string matching technique is neither robust nor fast. Hence several machine learning techniques are developed which are mainly based on classifier. These classifiers lack generalization capabilities which result in less performance leading to high false positives. However a specific attack can have wide range of signatures and a signature may of wide range of attacks. Therefore conventional classifiers like Neural Network needs frequent training when a new signature is discovered. Again discovery of such new signature also needs a regression with existing signature database. In order to avoid exploding the training nodes of Neural Network, it is important that a benchmark is set of introduce new nodes. In this paper we use Artificial Immune System mark the signatures as genes. A packet or network level signature is verified for closeness with existing model. In case of significant diversification is detected, the signature is marked as new which is regressed with the existing signature model to automate the grouping of the signature. Signature similar to existing ones is subjected to regression using PLS method and is then classified by neural network. This paper evaluates the performance of technique using the publicly available KDD Cup dataset and compares the result with conventional Neural Network Based Classifier, Support Vector Machine based Classifier, pure regression based technique and conventional string matching technique. Further we investigate the real time applicability of the technique by using PLS Regression to detect anomaly in CIT college router dataset. We consider that the connections by peer clients should only be accessing internet. Other activities like using Bittorrent are considered as anomaly. First we take router log and extract the features. We then select a specific data row and classify it using auto regression.

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An Agent Based Intrusion Detection System for Wireless Network with Artificial Immune System (AIS) and Negative Clone Selection

Cited by 14 publications

References 8 publications

LC-IDS: Loci-Constellation-Based Intrusion Detection for Reconfigurable Wireless Networks

LC-IDS: Loci-Constellation-Based Intrusion Detection for Reconfigurable Wireless Networks

Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism

A machine learning framework of neural network combined with PLS regression and artificial immune system for intrusion detection

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