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Microarchitectural attacks have become more threatening the hardware securitythan before with the increasing diversity of attacks such as Spectre andMeltdown. Vendor patches cannot keep up with the pace of the new threats, whichmakes the dynamic anomaly detection tools more evident than before.Unfortunately, previous studies utilize hardware performance counters that leadto high performance overhead and profile limited number of microarchitecturalattacks due to the small number of counters that can be profiled concurrently.This yields those detection tools inefficient in real-world scenarios.  In this study, we introduce MAD-EN dynamic detection tool that leveragessystem-wide energy consumption traces collected from a generic Intel RAPL toolto detect ongoing anomalies in a system. In our experiments, we show thatCNN-based MAD-EN can detect 10 different microarchitectural attacks with atotal of 15 variants with the highest F1 score of 0.999, which makes our toolthe most generic attack detection tool so far. Moreover, individual attacks canbe distinguished with a 98% accuracy after an anomaly is detected in a system.We demonstrate that MAD-EN introduces 69.3% less performance overhead comparedto performance counter-based detection mechanisms.

MAD-EN: Microarchitectural Attack Detection through System-wide Energy Consumption