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Award ID contains: 1816673

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  1. ; ; ; ; ; ( , International Conference on Dependable Systems and Networks)
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  2. ; ; ; ; ; ( , International Conference on Dependable Systems and Networks)
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  3. ; ; ; ; ; ; ( , RAID)
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  4. ; ; ( , ACM/IEEE International Conference on Cyber-Physical Systems)
    In this paper, we demonstrate the feasibility of smart malware that advances state-of-the-art attacks by (i) indirectly attacking a computing infrastructure through a cyber-physical system (CPS) that manages the environment in which the computing enterprise operates, (ii) disguising its malicious actions as accidental failures, and (iii) self-learning attack strategies from cyber-physical system measurement data. We address all aspects of the malware, including the construction of the self-learning malware and the launch of a failure injection attack. We validate the attacks in a data-driven CPS simulation environment developed as part of this study. 
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