Explainable Learning-Based Intrusion Detection Supported by Memristors
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Authors
Chen, Jingdi
Zhang, Lei
Riem, Joseph
Adam, Gina
Bastian, Nathaniel D.
Lan, Tian
Issue Date
2023
Type
proceedings-article
Language
Keywords
Artificial Intelligence and Robotics , Computer and Systems Architecture , Data Science , Hardware , Information Security
Alternative Title
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.
Description
Citation
J. Chen, L. Zhang, J. Riem, G. Adam, N. D. Bastian and T. Lan, "Explainable Learning-Based Intrusion Detection Supported by Memristors," 2023 IEEE Conference on Artificial Intelligence (CAI), Santa Clara, CA, USA, 2023, pp. 195-196, doi: 10.1109/CAI54212.2023.00092.
Publisher
IEEE