数据挖掘方法在入侵检测中的应用研究

Study of Data Mining in Intrusion Detection

针对入侵检测数据集具有的类别不均衡性、数据中心重叠、噪音、分布率变化等问题,提出一种集多种数据挖掘方法的解决方案,包括 k-means、C4.5、SVM、Nave Bayes、Bayes Net、Co-training 等,并进行相关实验.实验结果表明其有效性.

The solution based on multi-approaches of data mining involving k-means, C4.5, NaYve Bayes,Bayes net and Co-training is proposed in order to deal with the major problems of intrusion detection dataset such as class balance, class overlapping, noise, distributions etc. The experiment results show its validity.