关键词: 免疫算法, 支持向量机, 异常检测, 泛化能力, 亲和力

Abstract: In anomaly detection, utilizing support vector machines can make detection system have good generalization ability in situation of small sample. But appropriate parameters are very crucial to the learning results and generalization ability of support vector machines. And many irrelevant and redundant features degrade the performance of classification. Thus an approach that applied immune algorithm to optimize parameters of SVM(Support Vector Machine) and feature selection was proposed. Immune algorithm is an efficient random global optimization technique. It has nice performances such as avoiding local optimum, high precision solution, and quick convergence. The simulation results show that immune algorithm can improve the detection accuracy and meanwhile shorten the testing time.

Key words: immune algorithm, support vector machine, anomaly detection, generalization performance, affinity