A Negative Selection Algorithm Based on Hierarchical Clustering of Self Set and its Application in Anomaly Detection

Wen, Chenglu; Liu, Xiaojie; Li, Tao; Shi, Yuanquan; Zheng, Xufei; Hui, Zhao

doi:10.1080/18756891.2011.9727800

Cited by 16 publications

(2 citation statements)

References 12 publications

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“…Based on real valued NSA, two algorithms were abbreviated as HC-RNSA and CB-RNSA have been proposed in (Wen, 2011;Wen, 2013), the authors demonstrated that in high dimensional space, the Euclidian distance suffers from the dimension disaster, and the discrimination between self and non-self becomes harder. Therefore, they proposed fractional order distance to evaluate the matching degree between real vectors; the proposed algorithm can reduce the detector training cost.…”

Section: Nsa With Variable-sized Detectorsmentioning

confidence: 99%

A New Negative Selection Algorithm for Adaptive Network Intrusion Detection System

Chikhi

Chikh

2014

International Journal of Information Security and Privacy

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Negative Selection Algorithm (NSA) is one of the widely used techniques for Intrusion Detection Systems (IDS) designing. In this paper, the proposed is an IDS based on a new model of NSA namely HNSA-IDSA (Hybrid NSA for Intrusion Detection System Adaptation). The proposed system can detect unknown attacks; moreover can be adapted automatically when new profiles' changes of the system are detected. To determine the efficiency of the proposed approach, the standard KDD99 dataset was used for performing experiments. The obtained results show that the authors' mechanism outperforms some literature techniques providing variant important properties as high detection rate, low false positive, adaptability and new attacks detection.

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Section: Nsa With Variable-sized Detectorsmentioning

confidence: 99%

A New Negative Selection Algorithm for Adaptive Network Intrusion Detection System

Chikhi

Chikh

2014

International Journal of Information Security and Privacy

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“…Hamming matching rule was used to compute the affinity between antigens and immune cells (Distance=80). Since the intrusion detection system-inspired by artificial immune has many desirable attributes including self-learning, adaptability, diversity, which can well detect unknown attacks and approved in many researches 13,[15][16][17] , the emphases of the experiments are intrusion responses.…”

Section: Simulationsmentioning

confidence: 99%

An Immune-inspired Adaptive Automated Intrusion Response System Model

Peng¹,

Xie²,

Gao³

et al. 2012

IJCIS

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An immune-inspired adaptive automated intrusion response system model, named as MAIM, is proposed. The descriptions of self, non-self, immunocyte, memory detector, mature detector and immature detector of the network transactions, and the realtime network danger evaluation equations are given. Then, the automated response polices are adaptively performed or adjusted according to the realtime network danger. Thus, MAIM not only accurately evaluates the network attacks, but also greatly reduces the response times and response costs.

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Video Anomaly Detection Based on Hierarchical Clustering

Zhao

Wang

et al. 2019

Human Centered Computing

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A Negative Selection Algorithm Based on Hierarchical Clustering of Self Set and its Application in Anomaly Detection

Cited by 16 publications

References 12 publications

A New Negative Selection Algorithm for Adaptive Network Intrusion Detection System

A New Negative Selection Algorithm for Adaptive Network Intrusion Detection System

An Immune-inspired Adaptive Automated Intrusion Response System Model

Video Anomaly Detection Based on Hierarchical Clustering

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