Intrusion detection model based on coordinative immune and random antibody forest

This study aimed to deal with the problems that current intrusion detections have poor classification ability toward small sets of samples. A new intrusion detection model based on coordinative immune and random antibody forest (CIRAFID) is proposed. The vaccination mechanism of coordinative immune...

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Bibliographic Details
Published in:Journal of high speed networks 2022-01, Vol.28 (3), p.205-220
Main Authors: Zhang, Ling, Zhang, Jian-Wei, Xin, Xiang-Jun, Zhou, Kai-Lai
Format: Article
Language:English
Subjects:
Algorithms
Anomalies
Antibodies
Classification
Intrusion
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Summary:This study aimed to deal with the problems that current intrusion detections have poor classification ability toward small sets of samples. A new intrusion detection model based on coordinative immune and random antibody forest (CIRAFID) is proposed. The vaccination mechanism of coordinative immune algorithm is designed to increase the fitness of poor antibodies, a kind of random antibody detection forest model is given to detect anomalies, and to classify attacks. The experimental results show: the proposed model has higher detection rate, classification accuracy, classification ability and lower false positives rate.
ISSN:0926-6801
1875-8940
DOI:10.3233/JHS-220691