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Web"],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>Side-channel attack, which exploits time information leakage during the digital signature generation process, poses a severe threat to the confidentiality and integrity of transactions in blockchain payment channels. Currently, Transformer-based detection methods are effective at capturing long-term dependencies, while Long Short-Term Memory (LSTM) networks excel at modeling short-term dynamic time-series features. However, existing approaches struggle to uniformly model both long-term and short-term time-series features, limiting their performance in anomaly detection for complex transaction sequences. In this article, we propose TLD-SCA, a novel side-channel attack detection model that innovatively integrates Transformer\u2019s capability for global dependency modeling with LSTM\u2019s advantage in capturing local time-series dynamics. This enables long-term and short-term time-series analysis of transaction timing data. Experimental results demonstrate that TLD-SCA significantly outperforms existing methods in terms of accuracy (99.5%), precision (99.2%), and recall (98.3%), thereby providing a higher level of security assurance for blockchain payment channels.<\/jats:p>","DOI":"10.1145\/3771990","type":"journal-article","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T11:06:55Z","timestamp":1760612815000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["TLD-SCA: A Transformer-LSTM Detection Model against Side-Channel Attack in Blockchain Payment Channel"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1448-3619","authenticated-orcid":false,"given":"Tao","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science, Qufu Normal University","place":["Rizhao, China"]},{"name":"Guangdong Key Laboratory of Blockchain Security, Guangzhou University","place":["Rizhao, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1186-0295","authenticated-orcid":false,"given":"Fei","family":"Qiao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Qufu Normal University","place":["Rizhao, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3721-0702","authenticated-orcid":false,"given":"Yijia","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Qufu Normal University","place":["Rizhao, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8801-0715","authenticated-orcid":false,"given":"Kui","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Qufu Normal University","place":["Rizhao, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0616-2831","authenticated-orcid":false,"given":"Yan","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Computer Science, Qufu Normal University","place":["Rizhao, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4054-0549","authenticated-orcid":false,"given":"Yilei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Qufu Normal University","place":["Rizhao, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,2,18]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TENSYMP55890.2023.10223652"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.5755\/j02.eie.33995"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2024.3372018"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/ITNAC59571.2023.10368560"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/ITNAC59571.2023.10368563"},{"key":"e_1_3_1_7_2","article-title":"Performance counters to rescue: A machine learning based safeguard against micro-architectural side-channel-attacks","author":"Alam Manaar","year":"2017","unstructured":"Manaar Alam, Sarani Bhattacharya, Debdeep Mukhopadhyay, and Sourangshu Bhattacharya. 2017. 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