基于免疫算法与支持向量机的异常检测方法

Anomaly detection approach based on immune algorithm and support vector machine

在异常检测中,应用支持向量机算法能使检测系统在小样本的条件下具有良好的泛化能力。但支持向量机的参数取值决定了其学习性能和泛化能力,且大量无关或冗余的特征会降低分类的性能。基于此,提出了一种基于免疫算法的支持向量机参数和特征选择联合优化的方法。免疫算法是一种新的有效随机全局优化技术,它具有不易陷入局部最优、解的精度高、收敛速度快等优点。仿真结果表明算法在提高异常检测的检测正确率的同时相应的测试时间也在缩短。

In anomaly detection, utihzing 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.