A review on security analysis of cyber physical systems using Machine learning
| Autor: | Alshaibi Ahmed Jamal, Al-Ani Mustafa Majid, Alexander Alexandrovich Shelupanov, Anton Konev, Tatiana Kosachenko |
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| Rok vydání: | 2023 |
| Předmět: |
Adaptive strategies
Security analysis Security framework Computer science business.industry Deep learning Perspective (graphical) Cyber-physical system Globe General Medicine Machine learning computer.software_genre Cyber Space medicine.anatomical_structure medicine Artificial intelligence business computer |
| Zdroj: | Materials Today: Proceedings. 80:2302-2306 |
| ISSN: | 2214-7853 |
| DOI: | 10.1016/j.matpr.2021.06.320 |
| Popis: | The concept of Cyber Physical System (CPS) is widely used in different industries across the globe. In fact, it is the holistic approach towards dealing with cyber space and physical environments that do have inter-dependencies. In the existing systems, there was a separate approach for security of the two worlds (cyber and physical). However, it does not provide necessary security when security is employed independently. The research in this paper identifies the need for integrated security for CPS. Besides it throws light into different security challenges associated with CPS and the countermeasures that existed based on machine learning and deep learning techniques that come under Artificial Intelligence (AI) and data science. From the review of literature, it is understood that data science perspective is suitable for protecting CPS with required adaptive strategy. This paper provides several useful insights related to security analysis of CPS using machine learning. It paves way for further investigation and realize a comprehensive security framework to protect CPS from internal and external cyber-attacks. |
| Databáze: | OpenAIRE |
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