基于KNN离群点检测和随机森林的多层入侵检测方法

Intrusion detection system can efficiently detect attack behaviors, which will do great damage for network security. Currently many intrusion detection systems have low detection rates in these abnormal behaviors Probe (probing), U2R (user to root) and R2L (remote to local). Focusing on this weakness, a new hybrid multi-level intrusion detection method is proposed to identify network data as normal or abnormal behaviors. This method contains KNN (K nearest neighbors) outlier detection algorithm and multi-level random forests (RF) model, called KNN-RF. Firstly KNN outlier detection algorithm is applied to detect and delete outliers in each category and get a small high-quality training dataset. Then according to the similarity of network traffic, a new method of the division of data categories is put forward and this division method can avoid the mutual interference of anomaly behaviors in the detection process, especially for the detecting of the attack behaviors of small traffic. Based on this division, a multi-level random forests model is constructed to detect network abnormal behaviors and improve the efficiency of detecting known and unknown attacks. The popular KDD (knowledge discovery and data mining) Cup 1999 dataset is used to evaluate the performance of the proposed method. Compared with other algorithms, the proposed method is significantly superior to other algorithms in accuracy and detection rate, and can detect Probe, U2R and R2L effectively.