Real-Time Detection of Power Analysis Attacks by Machine Learning of Power Supply Variations On-Chip

摘要

Reliably and power efficiently securing integrated systems against advanced power analysis attacks (PAAs) is a significant design challenge in modern integrated circuits. Power masking and hiding are typical countermeasures for increasing system resilience for power attacks at the expense of the overall system performance and power efficiency. These method are, however, not able to alert the user or trigger additional protective actions in case of the attack. In this paper, a method for detecting power attacks in real-time is proposed. The proposed approach exploits statistical methods to analyze the on-chip voltage variations across an on-chip power grid and detect the attacker probe connected to the system. The problem of the full security coverage of the power grid is formulated and solved in this paper. Adjusting the density of the on-chip sensors and exploiting sparse analysis techniques is considered to simultaneously enhance the accuracy and power efficiency of the proposed solution. The proposed attack detection system is designed, simulated, and evaluated in Simulink based on IBM microprocessor benchmark data. Machine learning (ML) models are trained in Python and with scikit-learn ML library. The proposed system has been demonstrated to efficiently detect PAA within a period of time that is orders of magnitude shorter than a typical attack duration length. The system is expected to exhibit high detection accuracy and power efficiency across a wide spectrum of integrated systems.