{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T10:13:09Z","timestamp":1766139189208,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The application of deep learning in side-channel analysis faces critical challenges arising from dispersed public datasets\u2014i.e., datasets collected from heterogeneous sources and platforms with varying formats, labeling schemes, and sampling settings\u2014and insufficient sample distribution uniformity, characterized by imbalanced class distributions and long-tailed label samples. This paper presents a systematic analysis of symmetric cryptographic AES side-channel leakage datasets, examining how these issues impact the performance of deep learning-based side-channel analysis (DL-SCA) models. We analyze over 10 widely used datasets, including DPA Contest and ASCAD, and highlight key inconsistencies via visualization, statistical metrics, and model performance evaluations. For instance, the DPA_v4 dataset exhibits extreme label imbalance with a long-tailed distribution, while the ASCAD datasets demonstrate missing leakage features. Experiments conducted using CNN and Transformer models show that such imbalances lead to high accuracy for a few labels (e.g., label 14 in DPA_v4) but also extremely poor accuracy (&lt;0.5%) for others, severely degrading generalization. We propose targeted improvements through enhanced data collection protocols, training strategies, and feature alignment techniques. Our findings emphasize that constructing balanced datasets covering the full key space is vital to achieving robust and generalizable DL-SCA performance. This work contributes both empirical insights and methodological guidance for standardizing the design of side-channel datasets.<\/jats:p>","DOI":"10.3390\/sym17050769","type":"journal-article","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T08:56:55Z","timestamp":1747299415000},"page":"769","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Full-Element Analysis of Side-Channel Leakage Dataset on Symmetric Cryptographic Advanced Encryption Standard"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8069-3305","authenticated-orcid":false,"given":"Weifeng","family":"Liu","sequence":"first","affiliation":[{"name":"Artificial Intelligence and High-Speed Circuits Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenchang","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Solid-State Optoelectronic Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"College of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodong","family":"Cao","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and High-Speed Circuits Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihao","family":"Fu","sequence":"additional","affiliation":[{"name":"Multi-Agent Systems Research Center, School of Robotics, Beijing Union University, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juping","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Solid-State Optoelectronic Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"College of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8057-2444","authenticated-orcid":false,"given":"Jian","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Semiconductor Physics and Chip Technologies, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aidong","family":"Chen","sequence":"additional","affiliation":[{"name":"Multi-Agent Systems Research Center, School of Robotics, Beijing Union University, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanlong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Microelectronics Technology, Beijing 100076, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Microelectronics Technology, Beijing 100076, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Zhou","sequence":"additional","affiliation":[{"name":"Beijing Institute of Microelectronics Technology, Beijing 100076, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,15]]},"reference":[{"unstructured":"(2008, August 10). DPA Contest. Available online: https:\/\/dpacontest.telecom-paris.fr\/index.php.","key":"ref_1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/s13389-014-0075-9","article-title":"Practical improvements of side-channel attacks on AES: Feedback from the 2nd DPA contest","volume":"4","author":"Clavier","year":"2014","journal-title":"J. Cryptogr. Eng."},{"unstructured":"(2012, July 31). DPA contest v3. Available online: https:\/\/dpacontest.telecom-paris.fr\/v3\/index.php.","key":"ref_3"},{"unstructured":"(2013, July 09). DPA contest_v4. Available online: https:\/\/dpacontest.telecom-paris.fr\/v4\/index.php.","key":"ref_4"},{"doi-asserted-by":"crossref","unstructured":"Bhasin, S., Bruneau, N., Danger, J.-L., Guilley, S., and Najm, Z. (2014, January 18\u201322). Analysis and Improvements of the DPA Contest v4 Implementation. Proceedings of the Security, Privacy, and Applied Cryptography Engineering, Pune, India.","key":"ref_5","DOI":"10.1007\/978-3-319-12060-7_14"},{"unstructured":"Jean-S\u2019ebastien, C., and Ilya, K. (2021, April 14). AES_RD: Randomdelays-Traces. Available online: https:\/\/github.com\/ikizhvatov\/randomdelays-traces.","key":"ref_6"},{"key":"ref_7","first-page":"419","article-title":"An Efficient Method for Random Delay Generation in Embedded Software","volume":"2009","author":"Coron","year":"2009","journal-title":"IACR Cryptol. ePrint Arch."},{"unstructured":"Shivam Bhasin, D.J., Picek, S., and AES_HD (2018, July 13). Github Repository. Available online: https:\/\/github.com\/AESHD\/AES_HD_Dataset.","key":"ref_8"},{"unstructured":"Shivam Bhasin, D.J., and Picek, S. (2020, December 02). AES HD Dataset\u2014500,000 Traces. Github Repository. Available online: https:\/\/github.com\/AISyLab\/AES_HD.","key":"ref_9"},{"unstructured":"(2016, January 01). Northeastern University TeSCASE Dataset. Available online: https:\/\/chest.coe.neu.edu\/.","key":"ref_10"},{"unstructured":"Choudary, M.O., and Kuhn, M.G. (2017, December 22). Grizzly: Power-Analysis Traces for an 8-Bit Load Instruction. Available online: http:\/\/www.cl.cam.ac.uk\/research\/security\/datasets\/grizzly\/.","key":"ref_11"},{"unstructured":"PANDA-2018 (2019, June 17). Panda 2018 Challenge1. Available online: https:\/\/github.com\/kistoday\/Panda2018.","key":"ref_12"},{"unstructured":"Benadjila, R., Prouff, E., and Junwei, W. (2021, June 09). ASCAD (ANSSI SCA Database). Available online: https:\/\/github.com\/ANSSI-FR\/ASCAD.","key":"ref_13"},{"key":"ref_14","first-page":"53","article-title":"Study of Deep Learning Techniques for Side-Channel Analysis and Introduction to ASCAD Database","volume":"2018","author":"Prouff","year":"2018","journal-title":"IACR Cryptol. ePrint Arch."},{"doi-asserted-by":"crossref","unstructured":"Egger, M., Schamberger, T., Tebelmann, L., Lippert, F., and Sigl, G. (2022, January 11\u201312). A Second Look at the ASCAD Databases. Proceedings of the Constructive Side-Channel Analysis and Secure Design, Leuven, Belgium.","key":"ref_15","DOI":"10.1007\/978-3-030-99766-3_4"},{"unstructured":"Riscure (2019, January 30). CHES CTF. Available online: https:\/\/github.com\/agohr\/ches2018.","key":"ref_16"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"397","DOI":"10.46586\/tches.v2022.i4.397-437","article-title":"Breaking Masked Implementations of the Clyde-Cipher by Means of Side-Channel Analysis\u2014A Report on the CHES Challenge Side-Channel Contest 2020","volume":"2022","author":"Gohr","year":"2022","journal-title":"IACR Trans. Cryptogr. Hardw. Embed. Syst."},{"unstructured":"Bhasin, S., Chattopadhyay, A., Heuser, A., Jap, D., Picek, S., and Shrivastwa, R.R. (2020, January 01). Portability Dataset. Available online: http:\/\/aisylabdatasets.ewi.tudelft.nl\/.","key":"ref_18"},{"doi-asserted-by":"crossref","unstructured":"Weissbart, L., Picek, S., and Batina, L. (2019). One Trace Is All It Takes: Machine Learning-Based Side-Channel Attack on EdDSA, Springer.","key":"ref_19","DOI":"10.1007\/978-3-030-35869-3_8"},{"unstructured":"L\u00e9o Weissbart, S.P., and Batina, L. (2019, August 16). Ed25519 WolfSSL.Github Repository. Available online: https:\/\/github.com\/leoweissbart\/MachineLearningBasedSideChannelAttackonEdDSA.","key":"ref_20"},{"unstructured":"Chmielewski, \u0141. (2020, January 16). REASSURE (H2020 731591) ECC Dataset. Available online: https:\/\/zenodo.org\/records\/3609789.","key":"ref_21"},{"doi-asserted-by":"crossref","unstructured":"L\u00e9o Weissbart, \u0141.C., Picek, S., Batina, L., and Curve25519 Datasets (2020, October 13). Dropbox. Available online: https:\/\/www.dropbox.com\/s\/e2mlegb71qp4em3\/ecc_datasets.zip?dl=0.","key":"ref_22","DOI":"10.1007\/s41635-020-00106-w"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1007\/s41635-020-00106-w","article-title":"Systematic Side-Channel Analysis of Curve25519 with Machine Learning","volume":"4","author":"Weissbart","year":"2020","journal-title":"J. Hardw. Syst. Secur."},{"unstructured":"(2012, March 12). DPA Contest v4.1. Available online: https:\/\/dpacontest.telecom-paris.fr\/v4\/rsm_doc.php.","key":"ref_24"},{"unstructured":"(2015, July 20). DPA Contest_v4.2. Available online: https:\/\/dpacontest.telecom-paris.fr\/v4\/42_doc.php.","key":"ref_25"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"148","DOI":"10.46586\/tches.v2019.i3.148-179","article-title":"Make Some Noise. Unleashing the Power of Convolutional Neural Networks for Profiled Side-channel Analysis","volume":"2019","author":"Kim","year":"2019","journal-title":"IACR Trans. Cryptogr. Hardw. Embed. Syst."},{"key":"ref_27","first-page":"578","article-title":"Deep Learning based Side Channel Attacks in Practice","volume":"2019","author":"Maghrebi","year":"2019","journal-title":"IACR Cryptol. ePrint Arch."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/s13389-019-00220-8","article-title":"Deep learning for side-channel analysis and introduction to ASCAD database","volume":"10","author":"Benadjila","year":"2020","journal-title":"J. Cryptogr. Eng."},{"doi-asserted-by":"crossref","unstructured":"Paguada, S., and Armendariz, I. (2020, January 19\u201322). The Forgotten Hyperparameter: Introducing Dilated Convolution for Boosting CNN-Based Side-Channel Attacks. Proceedings of the Applied Cryptography and Network Security Workshops: ACNS 2020 Satellite Workshops, AIBlock, AIHWS, AIoTS, Cloud S&P, SCI, SecMT, and SiMLA, Rome, Italy. Proceedings.","key":"ref_29","DOI":"10.1007\/978-3-030-61638-0_13"},{"key":"ref_30","first-page":"1","article-title":"Methodology for Efficient CNN Architectures in Profiling Attacks","volume":"2020","author":"Habrard","year":"2019","journal-title":"IACR Trans. Cryptogr. Hardw. Embed. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"147","DOI":"10.46586\/tches.v2020.i3.147-168","article-title":"Revisiting a Methodology for Efficient CNN Architectures in Profiling Attacks","volume":"2020","author":"Wouters","year":"2020","journal-title":"IACR Trans. Cryptogr. Hardw. Embed. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s13389-019-00209-3","article-title":"Deep learning mitigates but does not annihilate the need of aligned traces and a generalized ResNet model for side-channel attacks","volume":"10","author":"Yuanyuan","year":"2020","journal-title":"J. Cryptogr. Eng."},{"key":"ref_33","first-page":"235","article-title":"Pay Attention to Raw Traces: A Deep Learning Architecture for End-to-End Profiling Attacks","volume":"2021","author":"Xiangjun","year":"2021","journal-title":"IACR Trans. Cryptogr. Hardw. Embed. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3215","DOI":"10.1109\/TIFS.2021.3076928","article-title":"Back to the Basics: Seamless Integration of Side-Channel Pre-Processing in Deep Neural Networks","volume":"16","author":"Xiaolu","year":"2021","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"doi-asserted-by":"crossref","unstructured":"Hajra, S., Saha, S., Alam, M., and Mukhopadhyay, D. (2022). TransNet: Shift Invariant Transformer Network for Side Channel Analysis, Springer.","key":"ref_35","DOI":"10.1007\/978-3-031-17433-9_16"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3863","DOI":"10.1109\/TIFS.2022.3216959","article-title":"Improving Deep Learning Based Second-Order Side-Channel Analysis With Bilinear CNN","volume":"17","author":"Pei","year":"2022","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"336","DOI":"10.46586\/tches.v2024.i1.336-374","article-title":"EstraNet: An Efficient Shift-Invariant Transformer Network for Side-Channel Analysis","volume":"2024","author":"Hajra","year":"2023","journal-title":"IACR Trans. Cryptogr. Hardw. Embed. Syst."},{"key":"ref_38","first-page":"4","article-title":"On the Performance of Deep Learning for Side-channel Analysis","volume":"2018","author":"Picek","year":"2018","journal-title":"IACR Cryptol. ePrint Arch."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"93","DOI":"10.47738\/jdmdc.v2i1.25","article-title":"Volatility and Risk Assessment of Blockchain Cryptocurrencies Using GARCH Modeling: An Analytical Study on Dogecoin, Polygon, and Solana","volume":"2","author":"Doan","year":"2025","journal-title":"J. Digit. Mark. Digit. Curr."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4296","DOI":"10.1109\/TIFS.2025.3558586","article-title":"Looking Clearer with Text: A Hierarchical Context Blending Network for Occluded Person Re-Identification","volume":"20","author":"Wang","year":"2025","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"113125","DOI":"10.1016\/j.knosys.2025.113125","article-title":"Learning discriminative topological structure information representation for 2D shape and social network classification via persistent homology","volume":"311","author":"Wang","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"113","DOI":"10.47738\/ijrm.v1i2.9","article-title":"Sales Trends and Price Determinants in the Virtual Property Market: Insights from Blockchain-Based Platforms","volume":"1","author":"Yadulla","year":"2024","journal-title":"Int. J. Res. Metaverse"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"48","DOI":"10.47738\/jcrb.v1i1.11","article-title":"Analyzing sentiment trends and patterns in bitcoin-related tweets using TF-IDF vectorization and k-means clustering","volume":"1","author":"Wahyuningsih","year":"2024","journal-title":"J. Curr. Res. Blockchain"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"157","DOI":"10.47738\/ijrm.v1i2.12","article-title":"Determinants of Virtual Property Prices in Decentraland an Empirical Analysis of Market Dynamics and Cryptocurrency Influence","volume":"1","author":"Wahyuningsih","year":"2024","journal-title":"Int. J. Res. Metaverse"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/5\/769\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:33:19Z","timestamp":1760031199000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/5\/769"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,15]]},"references-count":44,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["sym17050769"],"URL":"https:\/\/doi.org\/10.3390\/sym17050769","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2025,5,15]]}}}