{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T03:40:47Z","timestamp":1759722047788,"version":"build-2065373602"},"reference-count":28,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Circuit Theory &amp;amp; Apps"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>Deep learning\u2010based side\u2010channel attacks (DL\u2010SCA) have attracted widespread attention in recent years, and most of the researchers are devoted to finding the optimal DL\u2010SCA method. At the same time, traditional SCA methods have lost their luster. However, traditional attacks still have certain advantages. Compared with the DL\u2010SCA method, they do not require cumbersome engineering of tuning DL models and hyperparameters, making them easier to implement. Correlation power analysis (CPA), as a traditional SCA method, is still widely used in various analysis scenarios and plays an important role. In CPA, the leakage model is the key to simulating the power consumption, and it decides the attack efficiency. However, the existing leakage models are designed based on theory but ignore the actual attack scene. We found that conditional generative adversarial networks (CGAN) can ideally learn the target device's leakage characteristics and real power consumption. We let CGAN pre\u2010learn the leakage of the target device, and then make the generator as the leakage model \n. The \n leakage model can characterize the leakages of the device and consider the presence of noise in the actual scenario. It can map the power consumption more realistically and accurately, which can lead to a more powerful CPA attack. In this work, three kinds of \n leakage models (\n1, \n2, and \n3 leakage models) corresponding to the labels least significant bit (LSB), hamming weight (HW), and identity (ID) of CGAN are discussed. The experimental results show that the \n3 leakage model has better attack performance. Compared with the ordinary HW leakage model, the number of traces needed to recover the key on the ASCAD and SAKURA\u2010AES datasets reduced by about 38.9% and 85.9%, respectively.<\/jats:p>","DOI":"10.1002\/cta.4486","type":"journal-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T09:09:21Z","timestamp":1741684161000},"page":"5851-5861","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Stronger Leakage Model Based on Conditional Generative Adversarial Networks for Correlation Power Analysis"],"prefix":"10.1002","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3170-3740","authenticated-orcid":false,"given":"Cheng","family":"Tang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology Hengyang Normal University  Hengyang China"},{"name":"Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University  Hengyang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4832-4499","authenticated-orcid":false,"given":"Lang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology Hengyang Normal University  Hengyang China"},{"name":"Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University  Hengyang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Ou","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology Hengyang Normal University  Hengyang China"},{"name":"Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University  Hengyang China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,2,25]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.46586\/tches.v2022.i3.223-263"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.46586\/tches.v2022.i3.413-437"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3117091"},{"issue":"1","key":"e_1_2_11_5_1","first-page":"1","article-title":"Methodology for Efficient CNN Architectures in Profiling Attacks","volume":"2020","author":"Zaid G.","year":"2020","journal-title":"IACR Transactions on Cryptographic Hardware and Embedded Systems"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-023-05631-3"},{"issue":"1","key":"e_1_2_11_7_1","first-page":"209","article-title":"The Curse of Class Imbalance and Conflicting Metrics With Machine Learning for Side\u2010Channel Evaluations","volume":"2019","author":"Picek S.","year":"2019","journal-title":"IACR Transactions on Cryptographic Hardware and Embedded Systems"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.46586\/tches.v2020.i4.389-415"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.46586\/tches.v2022.i4.828-861"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.46586\/tches.v2020.i3.147-168"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.46586\/tches.v2021.i3.677-707"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.46586\/tches.v2021.i3.235-274"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-022-01588-6"},{"key":"e_1_2_11_14_1","unstructured":"P.Wang P.Chen Z.Luo et\u00a0al. \u201cEnhancing the Performance of Practical Profiling Side\u2010Channel Attacks Using Conditional Generative Adversarial Networks \u201d (2020) arXiv preprint arXiv:2007.05285."},{"issue":"1","key":"e_1_2_11_15_1","first-page":"125","article-title":"SCA\u2010CGAN: A New Side\u2010Channel Attack Method for Imbalanced Small Samples","volume":"32","author":"Wan W. 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G.","year":"2023","journal-title":"Radioengineering"},{"key":"e_1_2_11_16_1","doi-asserted-by":"crossref","unstructured":"N.Mukhtar L.Batina S.Picek andY.Kong \u201cFake It Till You Make It: Data Augmentation Using Generative Adversarial Networks for All the Crypto You Need on Small Devices \u201d inCryptographers' Track at the RSA Conference (Springer 2022):297\u2013321.","DOI":"10.1007\/978-3-030-95312-6_13"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.46586\/tches.v2019.i2.107-131"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.46586\/tches.v2021.i3.552-598"},{"key":"e_1_2_11_19_1","unstructured":"S.Karayalcin M.Krcek L.Wu S.Picek andG.Perin \u201cIt's a Kind of Magic: A Novel Conditional GAN Framework for Efficient Profiling Side\u2010Channel Analysis \u201d."},{"key":"e_1_2_11_20_1","unstructured":"I.Goodfellow J.Pouget\u2010Abadie M.Mirza et\u00a0al. \u201cGenerative Adversarial Nets \u201d."},{"key":"e_1_2_11_21_1","unstructured":"M.MirzaandS.Osindero \u201cConditional Generative Adversarial Nets \u201d (2014) arXiv preprint arXiv:1411.1784."},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2009.2019411"},{"key":"e_1_2_11_23_1","doi-asserted-by":"crossref","unstructured":"J.\u2010S.Coron C.Giraud E.Prouff S.Renner M.Rivain andP. 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