Research On Several Problems Of Artificial Immune Mechanisms

Biological immune system is a complex, highly parallel and self-adaptive informationsystem. Since it has characteristics of diversity, distributivity, dynamics, robustness and so on,it has attracted wide attention from the scientific community. Artificial immune system (AIS) isa kind of computing systems for resolving complicated problems, which is based on thebiological immune principle. AIS has been applied in optimization, information security,anomaly detection, fault diagnosis, pattern recognition and so on. It is becoming anotherresearch hotspot in the area of soft computing after artificial neural network and evolutionarycomputation.Based on the theoretical and experimental achievements of biological immune system, theprinciple of the immune algorithms for optimization and anomaly detection has been analyzed.The main achievements and innovative work of this dissertation can be included as follows.(1) Based on the immune danger theory, clonal selection theory and immune network, adanger theory based self-adaptive immune algorithm is proposed. In the algorithm, a novelimmune operator, named danger signal operator, is designed to guide the operation such asimmune proliferation, hypermutation and immune selection during the process of immuneresponse. The population density and the affinity between antibodies and antigens areconsidered to generate the danger signal. The experiments results show that the proposedalgorithm has a high ability in terms of multi-optimals searching, solution stability andconvergent speed.(2) The Baldwin effect based learning mechanism is generalized to the multiple Baldwineffect based mutation operator in order to provide more promising directions and to enhance theimmune evolutionary ability. The random selection framework and statistical basedself-adaptive framework are implemented to select the strategies in mutation strategy librarywhich consists of the Baldwin based mutation strategies and a random mutation strategy toparticipate in immune response respectively. Then the multiple Baldwin effect based immunealgorithm (MBCSA) and the self-adaptive multiple Baldwin effect based clonal selection algorithm(SAMBCSA) are proposed. Experimental results show that the proposed algorithms achieve abetter performance than Baldwin based clonal selection algorithm in terms of solution qualityand robustness. (3) An immune secondary response based mutation strategy and three modified DEmutation strategies, i.e. DE/rand/1,DE/rand/2,DE/current-to-rand/1, are designed to composea mutation strategy pool. The influences to single individual and to whole population of eachstrategy in mutation strategy pool are comprehensively considered. Then, a connected graphbased self-adaptive learning framework is implemented to adaptively select the strategies atdifferent stages for different problems, and on this basis a self-adaptive learning based immunealgorithm (SALIA) is proposed. Experimental results show that SALIA achieves a highuniversality and robustness.(4) On the basis of variable sized detector generation algorithm, a hybrid detector basednegative selection algorithm is proposed by introducing a concept of "big detector", which is ahyper-ring sphere in this dissertation. Experimental results show that the proposed algorithmachieves a high detection rate and a low false alarm rate. Besides, it has a smaller size ofdetector set than that of variable sized detector generation algorithm, which improves theefficiency of detection.(5) Based on the adaptive immune principle, negative selection and danger theory ofbiological immune system, an artificial immune based Trojan detection model is proposed. Inthis model, the innate immune system and adaptive immune system are combined by the dangersignal generated by the components. Five types of components are implemented tocooperatively detect the Trojans. Experimental results show that the proposed model has theability of detecting known and unknown Trojans.