Design of an Artificial Evolutionary Fuzzy Immune System for Computer Network Security
Abstract
An immune system is an exciting and efficient computational system with many applications in engineering, especially in intrusion detection systems. It is agent-based, distributed and selfadaptive and works based on the hierarchical layered architecture. In this paper, an agent-based artificial immune system based on computational intelligent techniques such as fuzzy logic and genetic algorithms is proposed for computer networks security. The proposed method uses the existing fuzzy relations between antigens and antibodies. Furthermore, it uses the genetic algorithms for optimizing antibodies. To simulate a typical network and to implement network attacks, the network simulator 2 (ns2) is used. To evaluate the performance of the proposed method, the DARPA standard data set is used in training and test phases. The proposed approach is compared with Forrest's immune system as a benchmark. Results show the proposed algorithm detects non-self entities at significantly higher rate, and detectors created by proposed algorithm are more diverse, more robust, and make fewer errors in attack detection.
Keywords
network security, fuzzy, evolutionary, immune system
