MLP-Based Power Analysis Attacks with Two-Point Joint Feature Selection

• Published in: 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) pp. 250 - 254
• Main Authors: Liyao, Ju, Tailiang, Chunlian, Zhang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.12.2020
• Subjects: Analytical models; Correlation; Correlation coefficient; cryptography; data classification; Feature extraction; learning (artificial intelligence); Machine learning; machine learning algorithms; MLP network; MLP-based power analysis attacks; multilayer perceptrons; power analysis attack method; Power demand; power traces; side-channel attacks; template attacks; two joint correlation coefficients; Two-point joint; two-point joint feature selection
• ISBN: 1665405031; 9781665405034
• ISSN: 2576-8964
• DOI: 10.1109/ICCWAMTIP51612.2020.9317303

The effective feature selection and classification of power traces in side-channel attacks has been a hot topic of research in recent years. Traditional side-channel attacks, such as simple power analysis attacks and template attacks, require a large number of power traces to break the key, while machine learning algorithms require fewer power traces to successfully recover the key, thus being able to greatly improve the success rate of the attack. Among them, the MLP network proves effective in data classification. Therefore, based on the MLP network, we investigate and propose a power analysis attack method with the feature selection of two joint correlation coefficients. Via comparing the conventional attack methods, the success rate of this attack method is significantly improved.