Visible to the public Q-Learning for Securing Cyber-Physical Systems : A survey

TitleQ-Learning for Securing Cyber-Physical Systems : A survey
Publication TypeConference Paper
Year of Publication2020
AuthorsAlabadi, Montdher, Albayrak, Zafer
Conference Name2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)
Keywordscomputer security, CPS, cps privacy, Cyber-physical systems, human factors, Internet of Things, machine learning, machine learning algorithms, privacy, pubcrawl, q-learning, Reinforcment Learning, security
AbstractA cyber-physical system (CPS) is a term that implements mainly three parts, Physical elements, communication networks, and control systems. Currently, CPS includes the Internet of Things (IoT), Internet of Vehicles (IoV), and many other systems. These systems face many security challenges and different types of attacks, such as Jamming, DDoS.CPS attacks tend to be much smarter and more dynamic; thus, it needs defending strategies that can handle this level of intelligence and dynamicity. Last few years, many researchers use machine learning as a base solution to many CPS security issues. This paper provides a survey of the recent works that utilized the Q-Learning algorithm in terms of security enabling and privacy-preserving. Different adoption of Q-Learning for security and defending strategies are studied. The state-of-the-art of Q-learning and CPS systems are classified and analyzed according to their attacks, domain, supported techniques, and details of the Q-Learning algorithm. Finally, this work highlight The future research trends toward efficient utilization of Q-learning and deep Q-learning on CPS security.
Citation Keyalabadi_q-learning_2020