@article{Ahmed_Salim_Hasan_2023, title={Deep Learning Method for Power Side-Channel Analysis on Chip Leakages}, volume={29}, url={https://eejournal.ktu.lt/index.php/elt/article/view/34650}, DOI={10.5755/j02.eie.34650}, abstractNote={<p>Power side channel analysis signal analysis is automated using deep learning. Signal processing and cryptanalytic techniques are necessary components of power side channel analysis. Chip leakages can be found using a classification approach called deep learning. In addition to this, we do this so that the deep learning network can automatically tackle signal processing difficulties such as re-alignment and noise reduction. We were able to break minimally protected Advanced Encryption Standard (AES), as well as masking-countermeasure AES and protected elliptic-curve cryptography (ECC). These results demonstrate that the attacker knowledge required for side channel analysis, which had previously placed a significant emphasis on human abilities, is decreasing. This research will appeal to individuals with a technical background who have an interest in deep learning, side channel analysis, and security.</p>}, number={6}, journal={Elektronika ir Elektrotechnika}, author={Ahmed, Amjed Abbas and Salim, Rana Ali and Hasan, Mohammad Kamrul}, year={2023}, month={Dec.}, pages={50-57} }