Tran, T. and Dam, D. and Dao, B. and Hoang, V. and Pham, C. and Hoang, T. (2023) Compacting Side-Channel Measurements With Amplitude Peak Location Algorithm. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. pp. 1-14. ISSN 10638210
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Nowadays, cryptographic algorithms are widely used to build safety mechanisms for specific objects in security services. Nevertheless, these algorithms are implemented in the hardware or software of the physical devices. Consequently, attackers will exploit physical information leakages, such as the device’s power consumption, and use them to get secret keys. The correlation power analysis (CPA) attack is a powerful and efficient cryptographic technique. The evaluation method, however, takes time because many traces are necessary to overcome designs protected by different countermeasures. Therefore, this article proposes a new technique to reduce the computation time by extracting the point of interest (POI) with an interpolation method. The proposal uses the local extreme value and two adjacent samples around it to interpolate the actual peak amplitude. Compared to the conventional CPA, the execution time in our solution is decreased by approximately 9.55 <inline-formula> <tex-math notation="LaTeX">×</tex-math> </inline-formula>, with only 53.32 of the given power traces used for attacking the masking design. Moreover, this technique can deal with the public desynchronized ASCAD database and has better results than recent alignment preprocessing methods. We apply the proposal in the preprocessing step before performing the previously non-profiled deep learning-based attacks. Our suggestion requires only 5000 traces, while the reported attacks fail or require more traces to recover the correct subkey. IEEE
Item Type: | Article |
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Divisions: | Offices > Office of International Cooperation |
Identification Number: | 10.1109/TVLSI.2023.3339810 |
Uncontrolled Keywords: | Deep learning; Electric power utilization, Complexity theory; Correlation; Correlation power analyse attack; Countermeasure; Non-profiled deep learning-based attack; Performances evaluation; Point of interest; Power; Power demands; Power measurement; Power traces; Preprocessing power trace; Proposal, Side channel attack |
Additional Information: | cited By 0 |
URI: | http://eprints.lqdtu.edu.vn/id/eprint/11056 |