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

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10.35377-saucis.04.01.834048-1424670.pdf

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2021-04-01

Author

GÜLMEZ, HALİM GÖRKEM
Angın, Pelin

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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.

Subject Keywords

security, deep reinforcement learning, intrusion detection

URI

http://dx.doi.org/10.35377/saucis.04.01.834048
https://hdl.handle.net/11511/97149

Journal

Sakarya University Journal of Computer and Information Sciences

DOI

https://doi.org/10.35377/saucis.04.01.834048

Collections

Department of Computer Engineering, Article

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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.