An Intrusion Detection Framework Based on Hybrid Multi-Level Data Mining

Abstract

With the dramatic opening-up of network, network security becomes a severe social problem with the rapid development of network technology. Intrusion Detection System (IDS) is an innovative and proactive network security technology, which becomes a hot topic in both industry and academia in recent years. There are four main characteristics of intrusion data that affect the performance of IDS including multicomponent, data imbalance, time-varying and unknown attacks. We propose a novel IDS framework called HMLD to address these issues, which is an exquisite designed framework based on Hybrid Multi-Level Data Mining. In this paper, we use KDDCUP99 dataset to evaluate the performance of HMLD. The experimental results show that HMLD can reach 96.70% accuracy which is nearly 1% higher than the recent proposed optimal algorithm SVM+ELM+Modified K-Means. In details, HMLD greatly increased the detection accuracy of DoS attacks and R2L attacks.