Online Detection of Spectre Attacks Using Microarchitectural Traces from Performance Counters

To improve processor performance, computer architects have adopted such acceleration techniques as speculative execution and caching. However, researchers have recently discovered that this approach implies inherent security flaws, as exploited by Meltdown and Spectre. Attacks targeting these vulnerabilities can leak protected data through side channels such as data cache timing by exploiting mis-speculated executions. The flaws can be catastrophic because they are fundamental and widespread and they affect many modern processors. Mitigating the effect of Meltdown is relatively straightforward in that it entails a software-based fix which has already been deployed by major OS vendors. However, to this day, there is no effective mitigation to Spectre. Fixing the problem may require a redesign of the architecture for conditional execution in future processors. In addition, a Spectre attack is hard to detect using traditional software-based antivirus techniques because it does not leave traces in traditional log files. In this paper, we proposed to monitor microarchitectural events such as cache misses, branch mispredictions from existing CPU performance counters to detect Spectre during attack runtime. Our detector was able to achieve 0% false negatives with less than 1 % false positives using various machine learning classifiers with a reasonable performance overhead.