
Cyber-physical systems (CPS) are vulnerable to adversarial attacks that can lead to catastrophic consequences. This survey examines existing recovery methods to restore CPS to desirable physical states after attacks, categorizing them as shallow (without dedicated recovery controllers) and deep (with dedicated recovery controllers) approaches.


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recovering-cyber-physical-systems-from-adversarial-attacks-shallow-deep-and-exploratory-approaches


Recovering Cyber-physical Systems from Adversarial Attacks: Shallow, Deep and Exploratory Approaches


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This work aims to develop a scalable and reconfigurable honeynet for cyber-physical systems (CPS) that can automatically generate diverse attacks to validate the system and produce datasets for training machine learning-based intrusion detection systems.


developing-a-scalable-and-reconfigurable-honeynet-for-securing-cyber-physical-systems


Developing a Scalable and Reconfigurable Honeynet for Securing Cyber-Physical Systems
