WARDEN: Warranting Robustness Against Deception in Next-Generation Systems
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Malicious users of a data center can reverse engineer power-management functions to exploit several power-management design issues. Despite hardware-enforced isolation, all three key security properties can be violated, namely confidentiality, integrity, and availability. Designing effective defenses against malicious actors for a robust and secure system thus requires engineering strong attacks. We propose an attack-pattern recognition system which is powered by machine learning (ML) and which consists of using error-correcting codes (ECCs) in order to detect the malicious workloads, thereby conferring robustness and security to power-management system design.
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