遗传支持向量机模型优化在入侵检测中的应用

Application in Intrusion Detection based on Genetic Support Vector Machine Model Optimization

针对目前入侵检测检测精度低的问题,根据遗传和支持向量机算法的特点,建立了一种遗传支持向量机模型。该模型首先用遗传算法优化支持向量机参数,再用优化后的支持向量机构建入侵检测模型,使用该模型进行入侵检测。实验通过讨论了支持向量机参数的选择对检测精度的影响,选取了合适的参数(c,σ)。结果表明,把这种遗传支持向量机模型用于入侵检测提高了检测精度。

Aiming at the problem of low accuracy in intrusion detection system,this paper established a genetic support vector machine（SVM） model according to the features of genetic algorithm and support vector machine algorithm.The model firstly optimizes the support vector parameters according to genetic algorithm;then we build the intrusion detection model with support vector machine optimized and use the model to detect.The experiments choose the proper parameters through discussing the influence of support vector machines parameters to the detection accuracy.The results show that putting genetic support vector machine model into intrusion detection improved detection accuracy.