NIGHTs-WATCH

Abstract

This paper presents a novel run-time detection mechanism, called NIGHTs-WATCH, for access-driven cache-based Side-Channel Attacks (SCAs). It comprises of multiple machine learning models, which use real-time data from hardware performance counters for detection. We perform experiments with two state-of-the-art SCAs (Flush+Reload and Flush+Flush) to demonstrate the detection capability and effectiveness of NIGHTs-WATCH. we provide experimental evaluation using realistic system load conditions and analyze results on detection accuracy, speed, system-wide performance overhead and confusion matrix for used models. Our results show detection accuracy of 99.51%, 99.50% and 99.44% for F+R attack in case of no, average and full load conditions, respectively, with performance overhead of < 2% at the highest detection speed, i.e., within 1% completion of a single RSA encryption round. In case of Flush+Flush, our results show 99.97%, 98.74% and 95.20% detection accuracy for no load, average load and full load conditions, respectively, with performance overhead of < 2% at the highest detection speed, i.e., within 12.5% completion of 400 AES encryption rounds needed to complete the attack. NIGHTs-WATCH shows considerably high detection efficiency under variable system load conditions.