The world has witnessed a fast-paced digital transformation in the past decade, giving rise to all-connected environments. While the increasingly widespread availability of networks has benefited many aspects of our lives, providing the necessary infrastructure for smart autonomous systems, it has also created a large cyber attack surface. This has made real-time network intrusion detection a significant component of any computerized system. With the advances in computer hardware architectures with fast, high-volume data processing capabilities and the developments in the field of artificial intelligence, deep learning has emerged as a significant aid for achieving accurate intrusion detection, especially for zero-day attacks. In this paper, we propose a deep reinforcement learning-based approach for network intrusion detection and demonstrate its efficacy using two publicly available intrusion detection datasets, namely NSL-KDD and UNSW-NB15. The experiment results suggest that deep reinforcement learning has significant potential to provide effective intrusion detection in the increasingly complex networks of the future.
Primary Language | English |
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Subjects | Computer Software |
Journal Section | Articles |
Authors | |
Publication Date | April 30, 2021 |
Submission Date | November 30, 2020 |
Acceptance Date | December 26, 2020 |
Published in Issue | Year 2021Volume: 4 Issue: 1 |
The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License