An Effective Network Intrusion Detection Framework Based on Learning to Hash

Abstract

Nowadays, the network intrusion detection has been an important issue in IoT. Although machine learning based methods seem to be promising in traditional network intrusion detection, these methods can hardly meet some demands of IoT. For example, unknown classes of flows are produced frequently in IoT, leading to classifiers training repeatly. To address this issue, we proposed a network intrusion detection framework based on learning to hash in this paper, which can reduce computation overhead significantly while avoiding frequent training of classifiers. The proposed framework consists of a hashing encoding module and an anomaly detection module with optimized k-NN classifier based on data distribution ratio. Moreover, the multi-index hashing is applied for fast and accurate search in Hamming space. Experimental results show that the proposed framework can detect various attacks and outperform the traditional intrusion detector.