Securing Data Center Against Power Attacks

Modern data centers employ complex and specialized power management architectures in the pursuit of energy and thermal efficiency. Interestingly, this rising complexity has exposed a new attack surface in an already vulnerable environment. In this work, we uncover a potent threat stemming from a compromised power management module in the hypervisor to motivate the need to safeguard the data centers from power attacks. HyperAttack—an internal power attack—maliciously increases the data center power consumption by more than 70%, while minimally affecting the service level agreement. We propose a machine learning-based secure architecture, SCALE, to detect anomalous power consumption behavior and prevent against power outages due to HyperAttack escalations. SCALE delivers 99% classification accuracy, with a maximum false positive rate of 3.8%.