A hybrid network intrusion detection technique using random forests

Abstract

Intrusion detection is important in network security. Most current network intrusion detection systems (NIDSs) employ either misuse detection or anomaly detection. However, misuse detection cannot detect unknown intrusions, and anomaly detection usually has high false positive rate. To overcome the limitations of both techniques, we incorporate both anomaly and misuse detection into the NIDS. In this paper, we present our framework of the hybrid system. The system combines the misuse detection and anomaly detection components in which the random forests algorithm is applied. We discuss the advantages of the framework and also report our experimental results over the KDD'99 dataset. The results show that the proposed approach can improve the detection performance of the NIDSs, where only anomaly or misuse detection technique is used.