Clonal selection approach for network intrusion detection
Felix T.S. Chan, Anusha Prakash, R.K. Tibrewal, Maharshi Tiwari · PolyU Institutional Research Archive (Hong Kong Polytechnic University) · 2013
Network attacks corrupt or destroy the information and services and it affect the integrity and confidentiality of network system. It can be classified as Denial of Service Attack (DoS), User to Root Attack (U2R), Remote to Local Attack (R2L) and Probing attack. In network security, a lot of researchers attract towards intrusion attacks and normal network traffic classification problem as it is a very challenging and critical problem. This paper presents an Artificial Immune System based approach for anomaly based network intrusion detection system. Artificial Immune System (AIS) algorithm is Meta-heuristic method which is used for clustering and pattern recognition. In addition, this article explains that the Clonal Selection Classification Algorithm (CSCA) can be applied to anomaly based network intrusion detection system and it can attain better solution only with very less number of antibodies. This model is compared with other approaches like Naive Bayes, Random Tree and Support Vector Machine (SVM) that have been used previously to solve the same problem.