A novel intrusion detection approach learned from the change of antibody concentration in biological immune response

摘要

Inspired by the relationship between the antibody concentration and the intrusion network traffic pattern intensity, we present a Novel Intrusion Detection Approach learned from the change of Antibody Concentration in biological immune response (NIDAAC) to reduce false alarm rate without affecting detection rate. In NIDAAC, the concepts and formal definitions of self, nonself, antibody, antigen and detector in the intrusion detection domain are given. Then, in initial IDS, new detectors are generated from the gene library and tested by the negative selection. In every effective IDS node, according to the intrusion network traffic pattern intensity, the change of antibody number is recorded from the process of clone proliferation based on the detector evolution. Finally, building upon the above works, a probabilistic calculation model for intrusion alarm production, which is based on the correlation between the antibody concentration and the intrusion network traffic pattern intensity, is proposed. Compared with Naive Bayes (NB), Multilevel Classifier (AdaBoost) and Hidden Markov Model (HMM), the false alarm rate of NIDAAC is reduced by 8.66%, 4.93% and 6.36%, respectively. Our theoretical analysis and experimental results show that NIDAAC has a better performance than previous approaches.