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Communication Dans Un Congrès Année : 2022

An Adaptive Attack Prediction Framework in Cyber-Physical Systems

Résumé

Cyber-physical systems (CPS) have been used in many applications, especially in smart cities and industrial systems. As a result of the exponential development of CPSs connected components, cyber-attacks against CPSs have exploded. Moreover, new critical CPS have high-security constraints that must be detected and predicted at an early stage of the communication process. Thus, it has become harder to detect these attacks. Machine learning is one of the most effective techniques for identifying and detecting CPS vulnerabilities. As a result of the heterogeneity of traffic and attacks, only one machine learning algorithm is unreliable. To provide a self-adaptive and scalable prediction/detection mechanism, we propose a framework called AAPF-CPS, which combines several machine learning algorithms with statistical tests. With multiple classification algorithms, AAPF-CPS analyzes CPS network logs simultaneously and in real-time. Friedman's test is also used to rank each classifier for each context in AAPF-CPS. Experimental results showed that AAPF-CPS could adapt ML algorithms based on traffic, allowing it to predict and detect potential attacks more efficiently.
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Dates et versions

hal-04065408 , version 1 (11-04-2023)

Identifiants

Citer

Mohamed-El-Amine Brahmia, Sami Babouche, Samir Ouchani, Mourad Zghal. An Adaptive Attack Prediction Framework in Cyber-Physical Systems. 2022 Ninth International Conference on Software Defined Systems (SDS), Dec 2022, Paris, France. pp.1-7, ⟨10.1109/SDS57574.2022.10062873⟩. ⟨hal-04065408⟩
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