Learning Based Anomaly Detection in Critical Cyber-Physical Systems

Cyber-physical system (CPS) has been one of the important parts of infrastructures and industrial control systems for years. Its footprint is seen in a wide range of domains including medical systems, automotive systems, environmental control, avionics, robotics, energy conservation, smart structure, etc. Due to the recent large-scale distribution, CPSs are more complicated than before, and they are facing a considerable number of challenges. For instance, the rate of uncertainty that they should handle has increased, and they have become more vulnerable to cyber-attacks. In order to manage unprecedented situations and uncertainty, CPS-run software should be able to control the operations of CPS, while remaining self-adaptive and goal-aware. Moreover, due to the devastating and irrecoverable consequences of CPS-specific attacks, there is a huge need to be able to detect and predict such attacks before happening. Anomaly detection is considered as a decent technique in identifying vulnerabilities in software systems. The best method that provides novel security technologies is machine-learning algorithms, which are widely used in anomaly detection in CPS networks. In this paper, different algorithms are implemented for detecting anomalies in order to confirm that machine learning and deep learning methods have strong potential to be used for attack detection in critical cyber- physical infrastructures.

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