Putting IMT to the Test: Revisiting and Expanding Interval Matching Techniques and their Calibration for SCA

Side-Channel Analysis (SCA) requires the detection of the specific time frame Cryptographic Operations (COs) take place in the side-channel signal. Under laboratory conditions with full control over the Device under Test (DuT), dedicated trigger signals can be implemented to indicate the start and end of COs. For real-world scenarios, waveform-matching techniques have been established which compare the side-channel signal with a template of the CO's pattern in real time to detect the CO in the side channel. State-of-the-Art approaches describe implementations based on Field-Programmable Gate Arrays (FPGAs). However, the maximal length of the template is restricted by the resources available on an FPGAs. Particularly, for high sampling rates the recording of an entire CO may need more samples than the maximum template length supported by a waveform-matching system. Consequently, the template has to be reduced such that it fits the resources while still containing all features relevant for detecting the COs via waveform matching.

In this paper, we introduce a generic interval-matching technique which provides several degrees of freedom for fine-tuning it to the statistical deviations of waveform measurements of COs. Moreover, we introduce a novel calibration method that finds the best parameters automatically based on statistical analysis of training data. Furthermore, we investigate a technique to reduce the number of features used for the interval matching by utilizing machine-learning-based feature extraction to find the most important samples in a template.

Finally, we evaluate the state-of-the-art interval matching and our expansions during calibration and during the application on a test set. The results show, that a reliable reduction to 10% of the original template size is possible with a reduction method from literature for our example. However, the combination of our proposed methods can reliably work with only 1.5% of the original size and is less volatile than the state-of-the-art approach for reducing the number of features.