{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T02:51:37Z","timestamp":1774666297811,"version":"3.50.1"},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"2","funder":[{"name":"MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program","award":["IITP-2024-2020-0-01789"],"award-info":[{"award-number":["IITP-2024-2020-0-01789"]}]},{"name":"Artificial Intelligence Convergence Innovation Human Resources Development","award":["IITP-2024-RS-2024-00254592"],"award-info":[{"award-number":["IITP-2024-RS-2024-00254592"]}]},{"name":"IITP"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Softw. Eng. Methodol."],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>Side-channel attacks (SCAs) exploit power analysis to extract secret information. Researchers have employed this technique to disassemble software and retrieve cryptographic keys by examining power consumption or electromagnetic emissions. They utilized hardware or Hamming-based fluctuations measurement to profile or model the power leakage. Developers employ power modeling to comprehend software leakage, although manually profiling the power trace across various devices and architectures requires time and effort. This work proposes a custom deep learning (DL) method to model the power trace. The DL model was trained to analyze how each assembly instruction produces leakage based on its context with other instructions. The proposed method can predict the power trace with 0.9963\u2009R\u00b2 from unseen assembly instructions. This method automates device leakage testing and captures contextual and non-linear relationships to help developers understand the software behavior, significantly reducing the time and effort required for power modeling. The potential impact of this DL model on software security is that it can effectively mitigate the risk of SCAs, thus enhancing the overall security of software systems.<\/jats:p>","DOI":"10.1145\/3734219","type":"journal-article","created":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T11:51:28Z","timestamp":1746445888000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Advanced Side-Channel Evaluation Using Contextual Deep Learning-Based Leakage Modeling"],"prefix":"10.1145","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7886-4082","authenticated-orcid":false,"given":"Saleh","family":"Alabdulwahab","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Dongguk University, Jung-gu, Korea (the Republic of)"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0511-7836","authenticated-orcid":false,"given":"JaeCheol","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Dongguk University, Jung-gu, Korea (the Republic of)"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3604-6580","authenticated-orcid":false,"given":"Young-Tak","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA and Department of Biomedical Sciences, Korea University College of Medicine, Seongbuk-gu, Korea (the Republic of)"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2580-4393","authenticated-orcid":false,"given":"Yunsik","family":"Son","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Dongguk University, Jung-gu, Korea (the Republic of)"}]}],"member":"320","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.126079"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13389-023-00340-2"},{"key":"e_1_3_1_4_2","unstructured":"Qinkun Bao and Dinghao Wu. 2021. 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