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The proliferation of ubiquitous computing requires energy-efficient as wellas secure operation of modern processors. Side channel attacks are becoming acritical threat to security and privacy of devices embedded in modern computinginfrastructures. Unintended information leakage via physical signatures such aspower consumption, electromagnetic emission (EM) and execution time haveemerged as a key security consideration for SoCs. Also, information publishedon purpose at user privilege level accessible through software interfacesresults in software only attacks. In this paper, we used a supervised learningbased approach for inferring applications executing on android platform basedon features extracted from EM side-channel emissions and software exposeddynamic voltage frequency scaling(DVFS) states. We highlight the importance ofmachine learning based approach in utilizing these multi-dimensional featureson a complex SoC, against profiling-based approaches. We also show thatlearning the instantaneous frequency states polled from onboard frequencydriver (cpufreq) is adequate to identify a known application and flagpotentially malicious unknown application. The experimental results onbenchmarking applications running on ARMv8 processor in Snapdragon 820 boarddemonstrates early detection of these apps, and atleast 85% accuracy indetecting unknown applications. Overall, the highlight is to utilize alow-complexity path to application inference attacks through learninginstantaneous frequency states pattern of CPU core.

Application Inference using Machine Learning based Side Channel Analysis