Side-channel signals have long been used in cryptanalysis, and recently they have also been utilized as a way to monitor program execution without involving the monitored system in its own monitoring. Both of these use-cases for side-channel analysis have seen steady improvement, allowing ever-smaller deviations in program behavior to be monitored (to track program behavior and/or identify anomalies) or exploited (to steal sensitive information). However, there is still very little intuition about where the limits for this are, e.g. whether a single-instruction or a single-bit difference can realistically be recovered from the signal.
In this paper, we use a popular open-source cryptographic software package as a test subject to demonstrate that, with enough training data, enough signal bandwidth, and enough signal-to-noise ratio, the decision of branch instructions that cause even single-instruction-differences in program execution can be recovered from the electromagnetic (EM) emanations of an IoT/embedded system. We additionally show that, in cryptographic implementations where branch decisions contain information about the secret key, nearly all such information can be extracted from the signal that corresponds to only a single cryptographic operation (e.g. encryption). Finally, we analyze how the received signal bandwidth, the amount of training, and the signal-to-noise ratio (SNR) affect the accuracy of side-channel-based reconstruction of individual branch decisions that occur during program execution.
Haider Adnan Khan, Monjur Alam, Alenka Zajic, and Milos Prvulovic, "Detailed tracking of program control flow using analog side-channel signals: a promise for IoT malware detection and a threat for many cryptographic implementations," Proc. SPIE 10630, Cyber Sensing 2018, 1063005 (Presented at SPIE Defense + Security: April 17, 2018; Published: 3 May 2018); https://doi.org/10.1117/12.2304382.
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