Reinforcement Learning for IoT Security: A Comprehensive Survey
暂无分享,去创建一个
[1] Alvin S. Lim,et al. Jamming and anti-jamming techniques in wireless networks: a survey , 2014, Int. J. Ad Hoc Ubiquitous Comput..
[2] Peter Dayan,et al. A Neural Substrate of Prediction and Reward , 1997, Science.
[3] Haibo He,et al. The sequential attack against power grid networks , 2014, 2014 IEEE International Conference on Communications (ICC).
[4] P SomanK.,et al. A short review on Applications of Deep learning for Cyber security , 2018, ArXiv.
[5] Sridhar Mahadevan,et al. Recent Advances in Hierarchical Reinforcement Learning , 2003, Discret. Event Dyn. Syst..
[6] Xianbin Wang,et al. Robust physical layer authentication using inherent properties of channel impulse response , 2011, 2011 - MILCOM 2011 Military Communications Conference.
[7] Pin Wan,et al. A survey of dynamic spectrum allocation based on reinforcement learning algorithms in cognitive radio networks , 2018, Artif. Intell. Rev..
[8] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 2005, IEEE Transactions on Neural Networks.
[9] Sudharman K. Jayaweera,et al. Reinforcement learning based anti-jamming with wideband autonomous cognitive radios , 2016, 2016 IEEE/CIC International Conference on Communications in China (ICCC).
[10] K. J. Ray Liu,et al. Anti-Jamming Games in Multi-Channel Cognitive Radio Networks , 2012, IEEE Journal on Selected Areas in Communications.
[11] Roberto Di Pietro,et al. Jamming mitigation in cognitive radio networks , 2013, IEEE Network.
[12] Yang Xiang,et al. A survey on security control and attack detection for industrial cyber-physical systems , 2018, Neurocomputing.
[13] Manuel López Martín,et al. Adversarial environment reinforcement learning algorithm for intrusion detection , 2019, Comput. Networks.
[14] Eylem Ekici,et al. Vehicular Networking: A Survey and Tutorial on Requirements, Architectures, Challenges, Standards and Solutions , 2011, IEEE Communications Surveys & Tutorials.
[15] Zhu Han,et al. A Survey on Applications of Model-Free Strategy Learning in Cognitive Wireless Networks , 2015, IEEE Communications Surveys & Tutorials.
[16] Imed Romdhani,et al. Architecting the Internet of Things: State of the Art , 2016 .
[17] Mazliza Othman,et al. Internet of Things security: A survey , 2017, J. Netw. Comput. Appl..
[18] Lalu Banoth,et al. A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection , 2017 .
[19] Nawal Alsaffar,et al. Smart Transportation System: A Review of Security and Privacy Issues , 2018, 2018 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT).
[20] Haibo He,et al. Q-Learning-Based Vulnerability Analysis of Smart Grid Against Sequential Topology Attacks , 2017, IEEE Transactions on Information Forensics and Security.
[21] Miao Wu,et al. Research on the architecture of Internet of Things , 2010, 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE).
[22] Moussa Ayyash,et al. Spectrum Assignment in Cognitive Radio Networks for Internet-of-Things Delay-Sensitive Applications Under Jamming Attacks , 2018, IEEE Internet of Things Journal.
[23] Theodore Tryfonas,et al. The Internet of Things: a security point of view , 2016, Internet Res..
[24] Jin-Ghoo Choi,et al. Channel hopping scheme to mitigate jamming attacks in wireless LANs , 2017, EURASIP J. Wirel. Commun. Netw..
[25] Weihua Zhuang,et al. Learning-Based Rogue Edge Detection in VANETs with Ambient Radio Signals , 2018, 2018 IEEE International Conference on Communications (ICC).
[26] Vijay Janapa Reddi,et al. Deep Reinforcement Learning for Cyber Security , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[27] Sophie Engle,et al. AN INTRODUCTION TO ARP SPOOFING , 2001 .
[28] Chunhua Wang,et al. Machine Learning and Deep Learning Methods for Cybersecurity , 2018, IEEE Access.
[29] Danda B. Rawat,et al. On the performance of cognitive internet-of-vehicles with unlicensed user-mobility and licensed user-activity , 2018, Comput. Networks.
[30] Tom Schaul,et al. Dueling Network Architectures for Deep Reinforcement Learning , 2015, ICML.
[31] Weihua Zhuang,et al. PHY-Layer Spoofing Detection With Reinforcement Learning in Wireless Networks , 2016, IEEE Transactions on Vehicular Technology.
[32] Jie Zhang,et al. On the Performance of Deep Reinforcement Learning-Based Anti-Jamming Method Confronting Intelligent Jammer , 2019, Applied Sciences.
[33] Alagan Anpalagan,et al. Anti-Jamming Communications Using Spectrum Waterfall: A Deep Reinforcement Learning Approach , 2017, IEEE Communications Letters.
[34] David Silver,et al. Deep Reinforcement Learning with Double Q-Learning , 2015, AAAI.
[35] Weihua Zhuang,et al. Learning-Based Rogue Edge Detection in VANETs with Ambient Radio Signals , 2018, ICC 2018.
[36] Saewoong Bahk,et al. Dodge-Jam: Anti-Jamming Technique for Low-Power and Lossy Wireless Networks , 2017, 2017 14th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).
[37] Joel J. P. C. Rodrigues,et al. On resilience of Wireless Mesh routing protocol against DoS attacks in IoT-based ambient assisted living applications , 2015, 2015 17th International Conference on E-health Networking, Application & Services (HealthCom).
[38] M. Rege,et al. Machine Learning for Cyber Defense and Attack , 2018 .
[39] Srikanth Kandula,et al. Resource Management with Deep Reinforcement Learning , 2016, HotNets.
[40] Liang Xiao,et al. DQN-Based Power Control for IoT Transmission against Jamming , 2018, 2018 IEEE 87th Vehicular Technology Conference (VTC Spring).
[41] Stuart Dreyfus,et al. Richard Bellman on the Birth of Dynamic Programming , 2002, Oper. Res..
[42] Aikaterini Mitrokotsa,et al. Classification of RFID Attacks , 2008, IWRT.
[43] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[44] Sergey Levine,et al. Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[45] Liang Xiao,et al. Anti-Jamming Underwater Transmission With Mobility and Learning , 2018, IEEE Communications Letters.
[46] Danda B. Rawat,et al. Vehicular Cyber Physical Systems: Adaptive Connectivity and Security , 2016 .
[47] Weihua Zhuang,et al. UAV Relay in VANETs Against Smart Jamming With Reinforcement Learning , 2018, IEEE Transactions on Vehicular Technology.
[48] Sungwook Kim. Cognitive Radio Anti-Jamming Scheme for Security Provisioning IoT Communications , 2015, KSII Trans. Internet Inf. Syst..
[49] Yi Zhou,et al. Understanding the Mirai Botnet , 2017, USENIX Security Symposium.
[50] Chong Li,et al. Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach , 2018, IEEE Transactions on Smart Grid.
[51] George C. Hadjichristofi,et al. Internet of Things: Security vulnerabilities and challenges , 2015, 2015 IEEE Symposium on Computers and Communication (ISCC).
[52] Danda B Rawat,et al. Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS , 2021, IEEE Communications Surveys & Tutorials.
[53] John Loch,et al. Using Eligibility Traces to Find the Best Memoryless Policy in Partially Observable Markov Decision Processes , 1998, ICML.
[54] Peter Henderson,et al. An Introduction to Deep Reinforcement Learning , 2018, Found. Trends Mach. Learn..
[55] D. Lalitha Bhaskari,et al. A Comprehensive Analysis of Spoofing , 2010 .
[56] Nicola Bezzo. Predicting Malicious Intention in CPS under Cyber-Attack , 2018, 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS).
[57] Mubashir Husain Rehmani,et al. Cognitive-Radio-Based Internet of Things: Applications, Architectures, Spectrum Related Functionalities, and Future Research Directions , 2017, IEEE Wireless Communications.
[58] Daniel Kudenko,et al. Multiagent Router Throttling: Decentralized Coordinated Response Against DDoS Attacks , 2013, IAAI.
[59] Weihua Zhuang,et al. Anti-Jamming Communication Game for UAV-Aided VANETs , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.
[60] Qiang Ni,et al. Application of reinforcement learning for security enhancement in cognitive radio networks , 2015, Appl. Soft Comput..
[61] Yuval Tassa,et al. Continuous control with deep reinforcement learning , 2015, ICLR.
[62] Gabriel Maciá-Fernández,et al. Anomaly-based network intrusion detection: Techniques, systems and challenges , 2009, Comput. Secur..
[63] Liang Xiao,et al. Learning-Based Privacy-Aware Offloading for Healthcare IoT With Energy Harvesting , 2019, IEEE Internet of Things Journal.
[64] Hung-Yu Chien,et al. Security of ultra-lightweight RFID authentication protocols and its improvements , 2007, OPSR.
[65] Eyal Amir,et al. Bayesian Inverse Reinforcement Learning , 2007, IJCAI.
[66] Weihua Zhuang,et al. User-Centric View of Unmanned Aerial Vehicle Transmission Against Smart Attacks , 2018, IEEE Transactions on Vehicular Technology.
[67] Jonathan M. Becker,et al. Dynamic Beamforming Optimization for Anti - Jamming and Hardware Fault Recovery , 2014 .
[68] Ali Dehghantanha,et al. Machine learning aided Android malware classification , 2017, Comput. Electr. Eng..
[69] Nicola Bezzo,et al. Reachability-based self-triggered scheduling and replanning of UAV operations , 2017, 2017 NASA/ESA Conference on Adaptive Hardware and Systems (AHS).
[70] Pramod K. Varshney,et al. Power control with jammer location uncertainty: A Game Theoretic perspective , 2014, 2014 48th Annual Conference on Information Sciences and Systems (CISS).
[71] Daniel S. Berman,et al. A Survey of Deep Learning Methods for Cyber Security , 2019, Inf..
[72] Srdjan Capkun,et al. Wormhole-Based Antijamming Techniques in Sensor Networks , 2007, IEEE Transactions on Mobile Computing.
[73] Vladimir Ulyantsev,et al. Applying Reinforcement Learning and Supervised Learning Techniques to Play Hearthstone , 2017, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA).
[74] Rodrigo Roman,et al. On the features and challenges of security and privacy in distributed internet of things , 2013, Comput. Networks.
[75] Daniel Kudenko,et al. Distributed response to network intrusions using multiagent reinforcement learning , 2015, Eng. Appl. Artif. Intell..
[76] Andrea Bonarini,et al. Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods , 2007, NIPS.
[77] Shivakant Mishra,et al. Defending against path-based DoS attacks in wireless sensor networks , 2005, SASN '05.
[78] Elyes Ben Hamida,et al. Security in Intelligent Transport Systems for Smart Cities: From Theory to Practice , 2016, Sensors.
[79] F.L. Lewis,et al. Reinforcement learning and adaptive dynamic programming for feedback control , 2009, IEEE Circuits and Systems Magazine.
[80] S. Biswas. Intrusion Detection Using Machine Learning: A Comparison Study , 2018 .
[81] Diksha G. Bhoyar,et al. Review of jamming attack using game theory , 2017, 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS).
[82] Max Mühlhäuser,et al. Towards Trustworthy Identity and Access Management for the Future Internet , 2010 .
[83] Liang Xiao,et al. IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security? , 2018, IEEE Signal Processing Magazine.
[84] K. Novak,et al. DNA repair: The guardian , 2003, Nature Reviews Cancer.
[85] Sudharman K. Jayaweera,et al. Multi-Agent Reinforcement Learning Based Cognitive Anti-Jamming , 2017, 2017 IEEE Wireless Communications and Networking Conference (WCNC).
[86] Walid Saad,et al. Jamming in the Internet of Things: A Game-Theoretic Perspective , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).
[87] Qi Hao,et al. A Survey on Software-Defined Network and OpenFlow: From Concept to Implementation , 2014, IEEE Communications Surveys & Tutorials.
[88] Zhen Zhu,et al. Study on the IOT Architecture and Gateway Technology , 2015, 2015 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES).
[89] Jianhua Li,et al. Deep Reinforcement Learning based Smart Mitigation of DDoS Flooding in Software-Defined Networks , 2018, 2018 IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD).
[90] Tanya L. Brewer,et al. Guidelines for Smart Grid Cybersecurity , 2014 .
[91] David K. Y. Yau,et al. Defending against distributed denial-of-service attacks with max-min fair server-centric router throttles , 2005, IEEE/ACM Transactions on Networking.
[92] Ying Chen,et al. Evaluation of Reinforcement Learning-Based False Data Injection Attack to Automatic Voltage Control , 2019, IEEE Transactions on Smart Grid.
[93] Ong Bi Lynn,et al. Internet of Things (IoT): Taxonomy of security attacks , 2016, 2016 3rd International Conference on Electronic Design (ICED).
[94] Yu Zhang,et al. Intelligent Cloud Resource Management with Deep Reinforcement Learning , 2018, IEEE Cloud Computing.
[95] Jiming Chen,et al. Smart community: an internet of things application , 2011, IEEE Communications Magazine.
[96] Sumeet Dua,et al. Data Mining and Machine Learning in Cybersecurity , 2011 .
[97] Liang Xiao,et al. Active authentication with reinforcement learning based on ambient radio signals , 2017, Multimedia Tools and Applications.
[98] Steve Mansfield-Devine,et al. Open source and the Internet of Things , 2018, Netw. Secur..
[99] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[100] H. Vincent Poor,et al. Two-dimensional anti-jamming communication based on deep reinforcement learning , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[101] Khin Mi Mi Aung,et al. A loss-free multipathing solution for data center network using software-defined networking approach , 2012, 2012 Digest APMRC.
[102] Young B. Moon,et al. Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods , 2017, Journal of Intelligent Manufacturing.
[103] Danda B. Rawat,et al. Securing space communication systems against reactive cognitive jammer , 2015, 2015 IEEE Wireless Communications and Networking Conference (WCNC).
[104] Zhen Ni,et al. A Multistage Game in Smart Grid Security: A Reinforcement Learning Solution , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[105] K. J. Ray Liu,et al. An anti-jamming stochastic game for cognitive radio networks , 2011, IEEE Journal on Selected Areas in Communications.
[106] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[107] Martin A. Riedmiller,et al. Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards , 2017, ArXiv.
[108] Xin Yuan,et al. Controlling IP Spoofing through Interdomain Packet Filters , 2008, IEEE Transactions on Dependable and Secure Computing.
[109] Alex Graves,et al. Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.
[110] R. B. Gopaluni,et al. Deep reinforcement learning approaches for process control , 2017, 2017 6th International Symposium on Advanced Control of Industrial Processes (AdCONIP).
[111] Sudharman K. Jayaweera,et al. Wideband Spectrum Sensing and Non-Parametric Signal Classification for Autonomous Self-Learning Cognitive Radios , 2012, IEEE Transactions on Wireless Communications.
[112] Joshua B. Tenenbaum,et al. Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation , 2016, NIPS.
[113] Peter Auer,et al. Finite-time Analysis of the Multiarmed Bandit Problem , 2002, Machine Learning.
[114] Yang Liu,et al. A survey on bad data injection attack in smart grid , 2013, 2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).
[115] Simon Haykin,et al. Anti-Jamming V2V Communication in an Integrated UAV-CAV Network with Hybrid Attackers , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).
[116] Nicola Bezzo,et al. An IRL Approach for Cyber-Physical Attack Intention Prediction and Recovery , 2018, 2018 Annual American Control Conference (ACC).
[117] Lida Xu,et al. The internet of things: a survey , 2014, Information Systems Frontiers.