SVMENCL:一种入侵检测的新方法(英文)

SVMENCL:An Intrusion Detection Method

在入侵检测中使用单个的支持向量机容易因"单点失效"而危害系统安全.提出一种基于支持向量机集成的方法来进行入侵检测.它采用负相关学习技术,在误差项中使用相关性惩罚因子使得生成的分类器有更好的多样性和精度;算法采用进化策略来自动地确定个体支持向量机的超参数,避免了需要了解问题的先验知识;最后,采用集成技术来组合个体支持向量机的检测结果.仿真实验表明这一方法有更好的检测性能,并且这种分布式并行检测方法有利于增加入侵检测系统的鲁棒性.

The individual SVM is prone to fail in the intrusion detection for the fragility of being attacked. This paper addresses a method using a support vector machines ensemble approach based on negative correlation learning for intrusion detection. Using a correlation penalty term in the error function, the aggregate members can be accurate and diverse. And the evolutionary strategy is considered as the best way to automatically determine the individual SVMs hyperparameters. At last we combine the results of all individual SVMs using ensemble technique. This distributed parallel detectioncan strengthen the robustness of the system. Simulation results show the effectiveness of the method presented in this paper.