Q-Learning for Securing Cyber-Physical Systems : A survey

Küçük Resim Yok

Tarih

2020

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Ieee

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

A 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.

Açıklama

2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) -- JUN 26-27, 2020 -- TURKEY

Anahtar Kelimeler

CPS, Q-Learning, Reinforcment Learning, Security

Kaynak

2nd International Congress On Human-Computer Interaction, Optimization and Robotic Applications (Hora 2020)

WoS Q Değeri

N/A

Scopus Q Değeri

N/A

Cilt

Sayı

Künye