doi: 10.7873/DATE.2015.0639

ABSTRACT

Side-channel analysis has become a wellestablished topic in the scientific community and industry over the last one and a half decade. Somewhat surprisingly, the vast majority of work on sidechannel analysis has been restricted to the “use case” of attacking cryptographic implementations through the recovery of keys. In this contribution, we show how sidechannel analysis can be used for extracting code from embedded systems based on a CPU's electromagnetic emanation. There are many applications within and outside the security community where this is desirable. In cryptography, it can, e.g., be used for recovering proprietary ciphers and security protocols. Another broad application field is general security and reverse engineering, e.g., for detecting IP violations of firmware or for debugging embedded systems when there is no debug interface or it is proprietary.
A core feature of our approach is that we take localized electromagnetic measurements that are spatially distributed over the IC being analyzed. Given these multiple inputs, we model code extraction as a classification problem that we solve with supervised learning algorithms. We apply a variant of linear discriminant analysis to distinguish between the multiple classes. In contrast to previous approaches, which reported instruction recognition rates between 40–70%, our approach detects more than 95% of all instructions for test code, and close to 90% for real-world code. The methods are thus very relevant for use in practice. Our method performs dynamic code recognition, which has both advantages (only the program parts that are actually executed are observed) but also limitations (rare code executions are difficult to observe).

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