Saad,A.M.S.E.Yildiz,B.Software Engineering2024-07-052024-07-0520230978-303127098-72367-337010.1007/978-3-031-27099-4_182-s2.0-85151057781https://doi.org/10.1007/978-3-031-27099-4_18https://hdl.handle.net/20.500.14411/4158Network-based technologies such as cloud computing, web services, and Internet of Things systems are becoming widely used due to their flexibility and preeminence. On the other hand, the exponential proliferation of network-based technologies exacerbated network security concerns. Intrusion takes an important share in the security concerns surrounding network-based technologies. Developing a robust intrusion detection system is crucial to solving the intrusion problem and ensuring the secure delivery of network-based technologies and services. In this paper, we propose a novel approach using deep reinforcement learning to detect intrusions to make network applications more secure, reliable, and efficient. As for the reinforcement learning approach, Deep Q-learning is used alongside a custom-built Gym environment that mimics network attacks and guides the learning process. The NSL-KDD dataset is used to create the reinforcement learning environment to train and evaluate the proposed model. The experimental results show that our proposed reinforcement learning approach outperforms other related solutions in the literature, achieving an accuracy that exceeds 93%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.eninfo:eu-repo/semantics/closedAccessDeep Q-learningIntrusion detection systemMachine learningNetwork securityOpenAI GymReinforcement learningConference ObjectQ4643 LNNS230243