TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Detecting Malware in Cyberphysical Systems Using Machine Learning: a Survey |
| 영문제목(English Title) |
Detecting Malware in Cyberphysical Systems Using Machine Learning: a Survey |
| 저자(Author) |
Mahdi Khosravy
Kazuaki Nakamura
Yuki Hirose
Naoko Nitta
Noboru Babaguchi
Montes F.
Bermejo J.
Sánchez L. E.
Bermejo J. R
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| 원문수록처(Citation) |
VOL 15 NO. 03 PP. 1119 ~ 1139 (2021. 03) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Among the scientific literature, it has not been possible to find a consensus on the definition of the limits or properties that allow differentiating or grouping the cyber-physical systems (CPS) and the Internet of Things (IoT). Despite this controversy the papers reviewed agree that both have become crucial elements not only for industry but also for society in general. The impact of a malware attack affecting one of these systems may suppose a risk for the industrial processes involved and perhaps also for society in general if the system affected is a critical infrastructure. This article reviews the state of the art of the application of machine learning in the automation of malware detection in cyberphysical systems, evaluating the most representative articles in this field and summarizing the results obtained, the most common malware attacks in this type of systems, the most promising algorithms for malware detection in cyberphysical systems and the future lines of research in this field with the greatest potential for the coming years.
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| 키워드(Keyword) |
Model Inversion Attack
Deep Learning
Face Recognition System
Media Clone
Cyber-physical System
IoT
Malware
Machine Learning
Detection
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| 파일첨부 |
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