Computer System Security Threat Evaluation Based Upon Artificial Immunity Model and Fuzzy Logic

Despite extensive efforts during recent years within the technical community to improve computer security, serious security problems continue to receive increasing coverage in both the popular and technical media. A large part of the problem stems from poor systems engineering of software and networks. In biological systems, natural internal immune system responses identify and protect the organism. A key mechanism in this immunity process is the ability to distinguish between self (i.e. normal organisms or behaviors) and non-self (i.e. abnormal or anomalous behavior). To deal with the ambiguities and imprecision in the process of anomaly detection for computer system security, we introduce a hierarchical fuzzy inference system to capture normal behavior deviations. Fuzzy logic has been widely used in control systems, decision-making, information retrieval, and many other applications. In this paper, we explore its capability in the area of computer security threat evaluation modeling and anomaly detection. Initial studies indicate promising results for this approach