Static Clonal Selection Algorithm Based on Match Range Model
Static Clonal Selection Algorithm (SCSA) is proposed to generate detectors to intrusion detection. A gene expression is used to express detector which exists as a form of classification rules. But full match rule is used and the gene expression can not express classification rules with OR operator freely. In this work, by combined the gene expression with partial match rule which is an important component in negative selection algorithm, a new expression which can express classification rules with OR operator is proposed. But the match threshold in match rule is difficult to set. Inspired from the T-cell maturation, a match range model is proposed. Base on this model and new expression proposed, a Static Clonal Selection Algorithm based on Match Range Model is proposed. The proposed algorithm is tested by simulation experiment for self/nonself discrimination. The results show that the proposed algorithm is more effective to generate detector with partial classification rules than SCSA which generates detector with full conjunctive rules with ‘and’; match range is self-adapted.
KeywordsIntrusion Detection Classification Rule Artificial Immune System Human Immune System Match Rule
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