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December 10, 2022

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Stacked Ensemble Model for Enhancing the DL based SCA

Hoang, A., Hanley, N., Khalid, A., Kundi, D., & O’Neill, M.

In di Vimercati, S. D. C. & Samarati, P., editors, Proceedings of the 19th International Conference on Security and Cryptography, SECRYPT 2022, Lisbon, Portugal, July 11-13, 2022, pages 59–68, 2022.

Deep learning (DL) has proven to be very effective for image recognition tasks, with a large body of research on various models for object classification. The application of DL to side-channel analysis (SCA) has already shown promising results, with experimentation on open-source variable key datasets showing that secret keys for block ciphers like Advanced Encryption Standard (AES)-128 can be revealed with 40 traces even in the presence of countermeasures. This paper aims to further improve the application of DL in SCA, by enhancing the power of DL when targeting the secret key of cryptographic algorithms when protected with SCA countermeasures. We propose a stacked ensemble model, which trains the output probabilities and Maximum likelihood score of multiple traces and/or sub-models to improve the performance of Convolutional Neural Network (CNN)-based models. Our model generates state-of-the art results when attacking the ASCAD variable-key database, which has a restricted number of training traces per key, recovering the key within 20 attack traces in comparison to 40 traces as required by the state-of-the-art CNN-based model with Plaintext feature extension (CNNP)-based model. During the profiling stage an attacker needs no additional knowledge of the implementation, such as the masking scheme or random mask values, only the ability to record the power consumption or electromagnetic field traces, plaintext/ciphertext and the key is needed. However, a two step training procedure is required. Additionally, no heuristic pre-processing is required in order to break the multiple masking countermeasures of the target implementation.

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