A Study on the Efficacy of Deep Reinforcement Learning for Intrusion Detection

Angın, Pelin
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.
Sakarya University Journal of Computer and Information Sciences


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Citation Formats
H. G. GÜLMEZ and P. Angın, “A Study on the Efficacy of Deep Reinforcement Learning for Intrusion Detection,” Sakarya University Journal of Computer and Information Sciences, vol. 4, no. 1, pp. 11–25, 2021, Accessed: 00, 2022. [Online]. Available: http://dx.doi.org/10.35377/saucis.04.01.834048.