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Connection Science Volume 27, 2015 - Issue 3: Adaptive Learning Agents, Part 3
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Articles

Distributed reinforcement learning for adaptive and robust network intrusion response

Kleanthis MalialisDepartment of Computer Science, University of York, York, UKCorrespondence[email protected]
,
Sam DevlinDepartment of Computer Science, University of York, York, UK
&
Daniel KudenkoDepartment of Computer Science, University of York, York, UK
Pages 234-252 | Received 08 Sep 2014, Accepted 05 Jan 2015, Published online: 15 Apr 2015

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Liu Xin, Liu Ziang, Zhang Yingli, Zhang Wenqiang, Lv Dong & Zhou Qingguo. (2022) TCN enhanced novel malicious traffic detection for IoT devices. Connection Science 34:1, pages 1322-1341.
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Shaokang Cai, Dezhi Han, Xinming Yin, Dun Li & Chin-Chen Chang. (2022) A Hybrid parallel deep learning model for efficient intrusion detection based on metric learning. Connection Science 34:1, pages 551-577.
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Lijun Xiao, Songyou Xie, Dezhi Han, Wei Liang, Jun Guo & Wen-Kuang Chou. (2021) A lightweight authentication scheme for telecare medical information system. Connection Science 33:3, pages 769-785.
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Articles from other publishers (13)

Paulina Stevia Nouwou Mindom, Amin Nikanjam & Foutse Khomh. (2023) A comparison of reinforcement learning frameworks for software testing tasks. Empirical Software Engineering 28:5.
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Xiaoxue Ma, Yun Li & Yan Gao. (2023) Decision model of intrusion response based on markov game in fog computing environment. Wireless Networks.
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Christophe Feltus. 2022. Research Anthology on Convergence of Blockchain, Internet of Things, and Security. Research Anthology on Convergence of Blockchain, Internet of Things, and Security 421 444 .
Kamalakanta Sethi, Y. Venu Madhav, Rahul Kumar & Padmalochan Bera. (2021) Attention based multi-agent intrusion detection systems using reinforcement learning. Journal of Information Security and Applications 61, pages 102923.
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Bizhu Wang, Yan Sun, Mengying Sun & Xiaodong Xu. (2021) Game-Theoretic Actor–Critic-Based Intrusion Response Scheme (GTAC-IRS) for Wireless SDN-Based IoT Networks. IEEE Internet of Things Journal 8:3, pages 1830-1845.
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Kamalakanta Sethi, E. Sai Rupesh, Rahul Kumar, Padmalochan Bera & Y. Venu Madhav. (2019) A context-aware robust intrusion detection system: a reinforcement learning-based approach. International Journal of Information Security 19:6, pages 657-678.
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Haodi Zhang, Jianye Hao & Xiaohong Li. (2020) A Method for Deploying Distributed Denial of Service Attack Defense Strategies on Edge Servers Using Reinforcement Learning. IEEE Access 8, pages 78482-78491.
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Zengguang Liu, Xiaochun Yin & Yuemei Hu. (2020) CPSS LR-DDoS Detection and Defense in Edge Computing Utilizing DCNN Q-Learning. IEEE Access 8, pages 42120-42130.
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Christophe Feltus. (2020) Current and Future RL’s Contribution to Emerging Network Security. Procedia Computer Science 177, pages 516-521.
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Taha Eghtesad, Yevgeniy Vorobeychik & Aron Laszka. 2020. Decision and Game Theory for Security. Decision and Game Theory for Security 58 79 .
Paulo C. Heredia & Shaoshuai Mou. (2019) Distributed Multi-Agent Reinforcement Learning by Actor-Critic Method. IFAC-PapersOnLine 52:20, pages 363-368.
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Patrick Mannion, Sam Devlin, Jim Duggan & Enda Howley. (2017) Multi-agent credit assignment in stochastic resource management games. The Knowledge Engineering Review 32.
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Lukman Adewale Ajao & Simon T. Apeh. (2022) Secure Edge Computing Vulnerabilities in Smart Cities Sustainability Using Petri Net and Genetic Algorithm-Based Reinforcement Learning. SSRN Electronic Journal.
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