基于粗集理论和SVM算法的入侵检测方法研究

Research of Intrusion Detection Method Based on Rough Set and SVM Algorithm

提出了一种将粗集方法与SVM算法结合起来的入侵检测方法。利用粗集理论在处理大数据量、消除冗余信息等方面的优势,减少SVM训练数据,克服SVM算法因为数据量大,处理速度慢等缺点。同时,借助于SVM良好的分类性能,对粗集约简后的最小属性子集进行分类,实现入侵检测的快速性能,高检测率和抗噪声强等优点。实验结果表明,该方法优于其它同类方式。

This paper proposes an intrusion detection method which combines rough set and SVM algorithm. In virtue of the ability rough set has to decease the amount of data and get rid of redundancy, the method can reduce amount of training data used and overcome SVM's defect of slow running speed when process large data set. At the same time, by the aid of SVM algorithm the method can classify the core of property set as to have extensiveness and high identification rate, and avoid disturbance. Experimental results show this method is better than other methods reported in the literature in terms of detection resolution.