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In International Workshop on\nRecent Advances in Intrusion Detection (pp. 36-54). Berlin, Heidelberg:\nSpringer Berlin Heidelberg, September 2003.","DOI":"10.1007\/978-3-540-45248-5_3"},{"key":"ref14","doi-asserted-by":"publisher","unstructured":"A. Ghubaish, Z. Yang and R. Jain, \u201cHDRL-IDS: A Hybrid Deep\nReinforcement Learning Intrusion Detection System for Enhancing\nthe Security of Medical Applications in 5G Networks,\u201d 2024 International Conference on Smart Applications, Communications and\nNetworking (SmartNets), Harrisonburg, VA, USA, 2024, pp. 1-6, https:\/\/dx.doi.org\/10.1109\/SmartNets61466.2024.10577692.","DOI":"10.1109\/SmartNets61466.2024.10577692"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Mahjoub, C., Hamdi, M., Alkanhel, R.I., Mohamed, S. and Ejbali,\nR., 2024. An adversarial environment reinforcement learning-driven\nintrusion detection algorithm for Internet of Things. EURASIP Journal\non Wireless Communications and Networking, 2024(1), p.21.","DOI":"10.1186\/s13638-024-02348-6"},{"key":"ref16","unstructured":"Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. , \u201cPlaying Atari with Deep Reinforcement\nLearning\u201d, 2013, ArXiv. \/abs\/1312.5602"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"Wolf, P., Hubschneider, C., Weber, M., Bauer, A., H\u00e4rtl, J., D\u00fcrr, F.\nand Z\u00f6llner, J.M., 2017, June. Learning how to drive in a real world\nsimulation with deep q-networks. In 2017 IEEE Intelligent Vehicles\nSymposium (IV) (pp. 244-250). 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