Explainable Learning-Based Intrusion Detection Supported by Memristors

Abstract

Deep learning based methods have demonstrated great success in network intrusion detection. However, the use of Deep Neural Networks (DNNs) makes it difficult to support real-time, packet-level detections in communication networks that handle high-speed traffic with low latency and energy. To this end, this paper proposes a novel approach to efficiently realize a DNN-based classifier by converting it into a pruned, explainable decision tree and evaluating its hardware implementation using an emerging architecture based on memristor devices, in order to support network intrusion detections on the fly. Preliminary experiments on real-world datasets show that the proposed method achieves nearly four orders of magnitude speed up while retaining the desired accuracy.