TY  - JOUR
TI  - A Study on the Efficacy of Deep Reinforcement Learning for Intrusion Detection
AB  - 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.
AU  - Gülmez, Halim Görkem
AU  - Angin, Pelin
DO  - 10.35377/saucis.04.01.834048
PY  - 2021
JO  - Sakarya University Journal of Computer and Information Sciences  (Online)
VL  - 4
IS  - 1
SN  - 2636-8129
SP  - 11
EP  - 25
DB  - TRDizin
UR  - http://search/yayin/detay/450865
ER  -