Research on Detector Generating Algorithm Based on Artificial Immune

The key of intrusion detection system based on artificial immune is generating the Detector set. To generate highly efficient one, we adopt the mutated Nonself-body set to generate candidate detectors and variable threshold R- continues bits matching rule to reduce the number of hole in mature detector, increase the tolerance of the candidate detector and the detector set, to result in mismatch detectors and increase detect ability. Theoretical analysis and experimental results show that the improved algorithm is effective and feasible.

[1]  Gregg H. Gunsch,et al.  An artificial immune system architecture for computer security applications , 2002, IEEE Trans. Evol. Comput..

[2]  Niu Dejiao.  Efficient Negative Selection Algorithm and its Analysis , 2009 .

[3]  Simon M. Garrett,et al.  How Do We Evaluate Artificial Immune Systems? , 2005, Evolutionary Computation.

[4]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[5]  Jinquan Zeng,et al.  A Novel Immunity-Based Anomaly Detection Method , 2008, 2008 International Seminar on Future BioMedical Information Engineering.

[6]  Nancy Forbes,et al.  Computer Immune Systems , 2005 .

[7]  Yao Xue-mei.  Improvement on Network Intrusion Detection Algorithm Based on Immunological Principle , 2008 .