Intrusion detection model based on coordinative immune and random antibody forest

Article type: Research Article

Authors: Zhang, Ling^{; *} | Zhang, Jian-Wei | Xin, Xiang-Jun | Zhou, Kai-Lai

Affiliations: Zhengzhou University of Light Industry, Zhengzhou, China

Correspondence: [*] Corresponding author: Ling Zhang, Zhengzhou University of Light Industry, 136 Hi-tech Zone, Science Avenue, Zhengzhou 450000, China. E-mail: [email protected].

Abstract: 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.

Keywords: Intrusion detection, antibody, vaccine, coordinative immune, random antibody forest

DOI: 10.3233/JHS-220691

Journal: Journal of High Speed Networks, vol. 28, no. 3, pp. 205-220, 2022