Unsupervised Horizontal Attacks against Public-Key Primitives with DCCA
- From Deep Canonical Correlation Analysis to Deep Collision Correlation Attacks -
Authors
Dorian Llavata, Eleonora Cagli, Rémi Eyraud, Vincent Grosso, Lilian Bossuet
Keywords:
Horizontal Collision Side-Channel Attacks
Single Trace Attacks
Non-Profiling Attacks
RSA
Exponentiation
Unsupervised Deep Learning
Abstract
In order to protect against side-channel attacks, masking countermeasure is widely considered. Its application on asymmetric cryptographic algorithms, such as RSA implementations, rendered multiple traces aggregation inefficient and led to the development of single trace horizontal attacks. Among these horizontal attacks proposed in the literature, many are based on the use of clustering techniques or statistical distinguishers to identify operand collisions. These attacks can be difficult to implement in practice, as they often require advanced trace pre-processing, including the selection of points of interest, a step that is particularly complex to perform in a non-profiling context. In recent years, numerous studies have shown the effectiveness of deep learning in security evaluation for conducting side-channel attacks. However, few attentions have been given to its application in asymmetric cryptography and horizontal attack scenarios. Additionally, the majority of deep learning attacks tend to focus on profiling attacks, which involve a supervised learning phase. In this paper, we propose a new non-profiling horizontal attack using an unsupervised deep learning method called Deep Canonical Correlation Analysis. In this approach, we propose to use a siamese neural network to maximize the correlation between pairs of modular operation traces through canonical correlation analysis, projecting them into a highly correlated latent space that is more suitable for identifying operand collisions. Several experimental results, on simulated traces and a protected RSA implementation with up-to-date countermeasures, show how our proposal outperformed state-of-the-art attacks despite being simpler to implement. This suggests that the use of deep learning can be impactful for security evaluators, even in a non-profiling context and in a fully unsupervised way.
References
[AABL13]
Galen Andrew, Raman Arora, Jeff A. Bilmes, and Karen Livescu. Deep Canonical Correlation Analysis. In Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA, 16-21 June 2013, volume 28 of JMLR Workshop and Conference Proceedings, pages 1247–1255. 2013. JMLR.org.
[AGM+13]
Olatz Arbelaitz, Ibai Gurrutxaga, Javier Muguerza, Jesús M. Pérez, and Iñigo Perona. An extensive comparative study of cluster validity indices. Pattern Recognit., 46(1):243–256, 2013. DOI: 10.1016/J.PATCOG.2012.07.021
[BC93]
Pierre Baldi and Yves Chauvin. Neural Networks for Fingerprint Recognition. Neural Comput., 5(3):402–418, 1993. DOI: 10.1162/NECO.1993.5.3.402
[BCM+22]
Alessandro Barenghi, Diego Carrera, Silvia Mella, Andrea Pace, Gerardo Pelosi, and Ruggero Susella. Profiled side channel attacks against the RSA cryptosystem using neural networks. J. Inf. Secur. Appl., 66:103122, 2022. DOI: 10.1016/J.JISA.2022.103122
[BCO04]
Eric Brier, Christophe Clavier, and Francis Olivier. Correlation Power Analysis with a Leakage Model. In Marc Joye and Jean-Jacques Quisquater, editors, CHES 2004, volume 3156 of LNCS, pages 16–29. August 2004. Springer, Heidelberg. DOI: 10.1007/978-3-540-28632-5_2
[BDK+25]
Sebastian Bitzer, Jeroen Delvaux, Elena Kirshanova, Sebastian Maaßen, Alexander May, and Antonia Wachter-Zeh. How to lose some weight: a practical template syndrome decoding attack. Designs, Codes and Cryptography, 2025. DOI: 10.1007/s10623-025-01603-1
[BJP+15]
Aurélie Bauer, Éliane Jaulmes, Emmanuel Prouff, Jean-René Reinhard, and Justine Wild. Horizontal collision correlation attack on elliptic curves - - Extended Version -. Cryptogr. Commun., 7(1):91–119, 2015. DOI: 10.1007/S12095-014-0111-8
[BJPW13]
Aurélie Bauer, Éliane Jaulmes, Emmanuel Prouff, and Justine Wild. Horizontal and Vertical Side-Channel Attacks against Secure RSA Implementations. In Ed Dawson, editor, CT-RSA 2013, volume 7779 of LNCS, pages 1–17. 2013. Springer, Heidelberg. DOI: 10.1007/978-3-642-36095-4_1
[BPS+20]
Ryad Benadjila, Emmanuel Prouff, Rémi Strullu, Eleonora Cagli, and Cécile Dumas. Deep learning for side-channel analysis and introduction to ASCAD database. Journal of Cryptographic Engineering, 10(2):163–188, June 2020. DOI: 10.1007/s13389-019-00220-8
[CCC+19]
Mathieu Carbone, Vincent Conin, Marie-Angela Cornélie, François Dassance, Guillaume Dufresne, Cécile Dumas, Emmanuel Prouff, and Alexandre Venelli. Deep Learning to Evaluate Secure RSA Implementations. IACR TCHES, 2019(2):132–161, 2019. https://tches.iacr.org/index.php/TCHES/article/view/7388 DOI: 10.13154/tches.v2019.i2.132-161
[CCJ04]
Benoît Chevallier-Mames, Mathieu Ciet, and Marc Joye. Low-Cost Solutions for Preventing Simple Side-Channel Analysis: Side-Channel Atomicity. IEEE Trans. Computers, 53(6):760–768, 2004. DOI: 10.1109/TC.2004.13
[CDP17]
Eleonora Cagli, Cécile Dumas, and Emmanuel Prouff. Convolutional Neural Networks with Data Augmentation Against Jitter-Based Countermeasures - Profiling Attacks Without Pre-processing. In Wieland Fischer and Naofumi Homma, editors, CHES 2017, volume 10529 of LNCS, pages 45–68. September 2017. Springer, Heidelberg. DOI: 10.1007/978-3-319-66787-4_3
[CFG+10]
Christophe Clavier, Benoit Feix, Georges Gagnerot, Mylène Roussellet, and Vincent Verneuil. Horizontal Correlation Analysis on Exponentiation. In Miguel Soriano, Sihan Qing, and Javier López, editors, ICICS 10, volume 6476 of LNCS, pages 46–61. December 2010. Springer, Heidelberg. DOI: 10.1007/978-3-642-17650-0_5
[CFG+12]
Christophe Clavier, Benoit Feix, Georges Gagnerot, Christophe Giraud, Mylène Roussellet, and Vincent Verneuil. ROSETTA for Single Trace Analysis. In Steven D. Galbraith and Mridul Nandi, editors, INDOCRYPT 2012, volume 7668 of LNCS, pages 140–155. December 2012. Springer, Heidelberg. DOI: 10.1007/978-3-642-34931-7_9
[CHL05]
Sumit Chopra, Raia Hadsell, and Yann LeCun. Learning a Similarity Metric Discriminatively, with Application to Face Verification. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), 20-26 June 2005, San Diego, CA, USA, pages 539–546. 2005. IEEE Computer Society. DOI: 10.1109/CVPR.2005.202
[Com90]
Paul G. Comba. Exponentiation Cryptosystems on the IBM PC. IBM Syst. J., 29(4):526–538, 1990. DOI: 10.1147/SJ.294.0526
[Cor99]
Jean-Sébastien Coron. Resistance against Differential Power Analysis for Elliptic Curve Cryptosystems. In Çetin Kaya Koç and Christof Paar, editors, CHES'99, volume 1717 of LNCS, pages 292–302. August 1999. Springer, Heidelberg. DOI: 10.1007/3-540-48059-5_25
[CRR03]
Suresh Chari, Josyula R. Rao, and Pankaj Rohatgi. Template Attacks. In Burton S. Kaliski Jr., Çetin Kaya Koç, and Christof Paar, editors, CHES 2002, volume 2523 of LNCS, pages 13–28. August 2003. Springer, Heidelberg. DOI: 10.1007/3-540-36400-5_3
[DBDM03]
Tijl De Bie and Bart De Moor. On the regularization of canonical correlation analysis. Int. Sympos. ICA and BSS, 2003.
[DLH+22]
Ngoc-Tuan Do, Phu-Cuong Le, Van-Phuc Hoang, Van-Sang Doan, Hoai Giang Nguyen, and Cong-Kha Pham. Mo-dlsca: Deep learning based non-profiled side channel analysis using multi-output neural networks. In 2022 International Conference on Advanced Technologies for Communications (ATC), pages 245–250. 2022. IEEE. DOI: 10.1109/atc55345.2022.9943024
[FV03]
Pierre-Alain Fouque and Frédéric Valette. The Doubling Attack - Why Upwards Is Better than Downwards. In Colin D. Walter, Çetin Kaya Koç, and Christof Paar, editors, Cryptographic Hardware and Embedded Systems - CHES 2003, 5th International Workshop, Cologne, Germany, September 8-10, 2003, Proceedings, volume 2779 of Lecture Notes in Computer Science, pages 269–280. 2003. Springer. DOI: 10.1007/978-3-540-45238-6_22
[GBB06]
Ursula Gonzales-Barron and Francis Butler. A comparison of seven thresholding techniques with the K-means clustering algorithm for measurement of bread-crumb features by digital image analysis. Journal of food engineering, 74(2):268–278, 2006. DOI: 10.1016/j.jfoodeng.2005.03.007
[GBC16]
Ian J. Goodfellow, Yoshua Bengio, and Aaron C. Courville. Deep Learning. Adaptive computation and machine learning. MIT Press 2016.
[GCCD23]
Vincent Grosso, Pierre-Louis Cayrel, Brice Colombier, and Vlad-Florin Dragoi. Punctured Syndrome Decoding Problem - Efficient Side-Channel Attacks Against Classic McEliece. In Elif Bilge Kavun and Michael Pehl, editors, Constructive Side-Channel Analysis and Secure Design - 14th International Workshop, COSADE 2023, Munich, Germany, April 3-4, 2023, Proceedings, volume 13979 of Lecture Notes in Computer Science, pages 170–192. 2023. Springer. DOI: 10.1007/978-3-031-29497-6_9
[GLWS20]
Quanxue Gao, Huanhuan Lian, Qianqian Wang, and Gan Sun. Cross-Modal Subspace Clustering via Deep Canonical Correlation Analysis. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pages 3938–3945. 2020. AAAI Press. DOI: 10.1609/AAAI.V34I04.5808
[GMO01]
Karine Gandolfi, Christophe Mourtel, and Francis Olivier. Electromagnetic Analysis: Concrete Results. In Çetin Kaya Koç, David Naccache, and Christof Paar, editors, CHES 2001, volume 2162 of LNCS, pages 251–261. May 2001. Springer, Heidelberg. DOI: 10.1007/3-540-44709-1_21
[HIM+13]
Johann Heyszl, Andreas Ibing, Stefan Mangard, Fabrizio De Santis, and Georg Sigl. Clustering Algorithms for Non-profiled Single-Execution Attacks on Exponentiations. In Aurélien Francillon and Pankaj Rohatgi, editors, Smart Card Research and Advanced Applications - 12th International Conference, CARDIS 2013, Berlin, Germany, November 27-29, 2013. Revised Selected Papers, volume 8419 of Lecture Notes in Computer Science, pages 79–93. 2013. Springer. DOI: 10.1007/978-3-319-08302-5_6
[HKT15]
Neil Hanley, HeeSeok Kim, and Michael Tunstall. Exploiting Collisions in Addition Chain-Based Exponentiation Algorithms Using a Single Trace. In Kaisa Nyberg, editor, CT-RSA 2015, volume 9048 of LNCS, pages 431–448. April 2015. Springer, Heidelberg. DOI: 10.1007/978-3-319-16715-2_23
[HMA+08]
Naofumi Homma, Atsushi Miyamoto, Takafumi Aoki, Akashi Satoh, and Adi Shamir. Collision-Based Power Analysis of Modular Exponentiation Using Chosen-Message Pairs. In Elisabeth Oswald and Pankaj Rohatgi, editors, Cryptographic Hardware and Embedded Systems - CHES 2008, 10th International Workshop, Washington, D.C., USA, August 10-13, 2008. Proceedings, volume 5154 of Lecture Notes in Computer Science, pages 15–29. 2008. Springer. DOI: 10.1007/978-3-540-85053-3_2
[HOM06]
Christoph Herbst, Elisabeth Oswald, and Stefan Mangard. An AES Smart Card Implementation Resistant to Power Analysis Attacks. In Jianying Zhou, Moti Yung, and Feng Bao, editors, ACNS 06, volume 3989 of LNCS, pages 239–252. June 2006. Springer, Heidelberg. DOI: 10.1007/11767480_16
[HZRS16]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, pages 770–778. 2016. IEEE Computer Society. DOI: 10.1109/CVPR.2016.90
[IS15]
Sergey Ioffe and Christian Szegedy. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In Francis R. Bach and David M. Blei, editors, Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015, volume 37 of JMLR Workshop and Conference Proceedings, pages 448–456. 2015. JMLR.org.
[ISW03]
Yuval Ishai, Amit Sahai, and David Wagner. Private Circuits: Securing Hardware against Probing Attacks. In Dan Boneh, editor, CRYPTO 2003, volume 2729 of LNCS, pages 463–481. August 2003. Springer, Heidelberg. DOI: 10.1007/978-3-540-45146-4_27
[JSZK15]
Max Jaderberg, Karen Simonyan, Andrew Zisserman, and Koray Kavukcuoglu. Spatial Transformer Networks. In Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett, editors, Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, pages 2017–2025. 2015.
[JY03]
Marc Joye and Sung-Ming Yen. The Montgomery Powering Ladder. In Burton S. Kaliski Jr., Çetin Kaya Koç, and Christof Paar, editors, CHES 2002, volume 2523 of LNCS, pages 291–302. August 2003. Springer, Heidelberg. DOI: 10.1007/3-540-36400-5_22
[JYP17]
Zhong Ji, Xuejie Yu, and Yanwei Pang. Zero-Shot Learning with Deep Canonical Correlation Analysis. In Jinfeng Yang, Qinghua Hu, Ming-Ming Cheng, Liang Wang, Qingshan Liu, Xiang Bai, and Deyu Meng, editors, Computer Vision - Second CCF Chinese Conference, CCCV 2017, Tianjin, China, October 11-14, 2017, Proceedings, Part III, volume 773 of Communications in Computer and Information Science, pages 209–219. 2017. Springer. DOI: 10.1007/978-981-10-7305-2_19
[Kar63]
Anatolii Karatsuba. Multiplication of multidigit numbers on automata. In Soviet physics doklady, volume 7, pages 595–596. 1963.
[Kau18]
Eric Kauderer-Abrams. Quantifying Translation-Invariance in Convolutional Neural Networks. CoRR, abs/1801.01450, 2018.
[Ket71]
Jon R Kettenring. Canonical analysis of several sets of variables. Biometrika, 58(3):433–451, 1971.
[KJJ99]
Paul C. Kocher, Joshua Jaffe, and Benjamin Jun. Differential Power Analysis. In Michael J. Wiener, editor, CRYPTO'99, volume 1666 of LNCS, pages 388–397. August 1999. Springer, Heidelberg. DOI: 10.1007/3-540-48405-1_25
[KUMH17]
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter. Self-Normalizing Neural Networks. In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett, editors, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 971–980. 2017.
[LF00]
Pei Ling Lai and Colin Fyfe. Kernel and Nonlinear Canonical Correlation Analysis. Int. J. Neural Syst., 10(5):365–377, 2000. DOI: 10.1142/S012906570000034X
[LHK22]
Nayeon Lee, Seokhie Hong, and Heeseok Kim. Single-Trace Attack Using One-Shot Learning With Siamese Network in Non-Profiled Setting. IEEE Access, 10:60778–60789, 2022. DOI: 10.1109/ACCESS.2022.3180742
[LHRB16]
Shan Sung Liew, Mohamed Khalil Hani, Syafeeza Ahmad Radzi, and Rabia Bakhteri. Gender classification: a convolutional neural network approach. Turkish Journal of Electrical Engineering and Computer Sciences, 24(3):1248–1264, 2016. DOI: 10.3906/elk-1311-58
[LLO24]
Di Li, Lang Li, and Yu Ou. Side-channel analysis based on Siamese neural network. J. Supercomput., 80(4):4423–4450, 2024. DOI: 10.1007/S11227-023-05631-3
[LQZL19]
Wei Liu, Jie-Lin Qiu, Wei-Long Zheng, and Bao-Liang Lu. Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis. CoRR, abs/1908.05349, 2019.
[LY09]
Dongju Liu and Jian Yu. Otsu Method and K-means. In Ge Yu, Mario Köppen, Shyi-Ming Chen, and Xiamu Niu, editors, 9th International Conference on Hybrid Intelligent Systems (HIS 2009), August 12-14, 2009, Shenyang, China, pages 344–349. 2009. IEEE Computer Society. DOI: 10.1109/HIS.2009.74
[MBPK22]
Naila Mukhtar, Lejla Batina, Stjepan Picek, and Yinan Kong. Fake It Till You Make It: Data Augmentation Using Generative Adversarial Networks for All the Crypto You Need on Small Devices. In Steven D. Galbraith, editor, Topics in Cryptology - CT-RSA 2022 - Cryptographers' Track at the RSA Conference 2022, Virtual Event, March 1-2, 2022, Proceedings, volume 13161 of Lecture Notes in Computer Science, pages 297–321. 2022. Springer. DOI: 10.1007/978-3-030-95312-6_13
[MM79]
Nick Martin and Hermine Maes. Multivariate analysis. London, UK: Academic, 1979.
[MMD20]
Coenraad Mouton, Johannes C. Myburgh, and Marelie H. Davel. Stride and Translation Invariance in CNNs. In Aurona J. Gerber, editor, Artificial Intelligence Research - First Southern African Conference for AI Research, SACAIR 2020, Muldersdrift, South Africa, February 22-26, 2021, Proceedings, volume 1342 of Communications in Computer and Information Science, pages 267–281. 2020. Springer. DOI: 10.1007/978-3-030-66151-9_17
[Mon85]
Peter L Montgomery. Modular multiplication without trial division. Mathematics of computation, 44(170):519–521, 1985.
[MPP16]
Houssem Maghrebi, Thibault Portigliatti, and Emmanuel Prouff. Breaking Cryptographic Implementations Using Deep Learning Techniques. In Claude Carlet, M. Anwar Hasan, and Vishal Saraswat, editors, Security, Privacy, and Applied Cryptography Engineering - 6th International Conference, SPACE 2016, Hyderabad, India, December 14-18, 2016, Proceedings, volume 10076 of Lecture Notes in Computer Science, pages 3–26. 2016. Springer. DOI: 10.1007/978-3-319-49445-6_1
[NBS22]
Meenal V. Narkhede, Prashant P. Bartakke, and Mukul S. Sutaone. A review on weight initialization strategies for neural networks. Artif. Intell. Rev., 55(1):291–322, 2022. DOI: 10.1007/S10462-021-10033-Z
[NF95]
Beat E Neuenschwander and Bernard D Flury. Common canonical variates. Biometrika, 82(3):553–560, 1995.
[PC15]
Guilherme Perin and Lukasz Chmielewski. A Semi-Parametric Approach for Side-Channel Attacks on Protected RSA Implementations. In Naofumi Homma and Marcel Medwed, editors, Smart Card Research and Advanced Applications - 14th International Conference, CARDIS 2015, Bochum, Germany, November 4-6, 2015. Revised Selected Papers, volume 9514 of Lecture Notes in Computer Science, pages 34–53. 2015. Springer. DOI: 10.1007/978-3-319-31271-2_3
[PCBP21]
Guilherme Perin, Łukasz Chmielewski, Lejla Batina, and Stjepan Picek. Keep it Unsupervised: Horizontal Attacks Meet Deep Learning. IACR TCHES, 2021(1):343–372, 2021. https://tches.iacr.org/index.php/TCHES/article/view/8737 DOI: 10.46586/tches.v2021.i1.343-372
[PITM14]
Guilherme Perin, Laurent Imbert, Lionel Torres, and Philippe Maurine. Attacking Randomized Exponentiations Using Unsupervised Learning. In Emmanuel Prouff, editor, COSADE 2014, volume 8622 of LNCS, pages 144–160. April 2014. Springer, Heidelberg. DOI: 10.1007/978-3-319-10175-0_11
[PSG16]
Romain Poussier, François-Xavier Standaert, and Vincent Grosso. Simple Key Enumeration (and Rank Estimation) Using Histograms: An Integrated Approach. In Benedikt Gierlichs and Axel Y. Poschmann, editors, CHES 2016, volume 9813 of LNCS, pages 61–81. August 2016. Springer, Heidelberg. DOI: 10.1007/978-3-662-53140-2_4
[PZS17]
Romain Poussier, Yuanyuan Zhou, and François-Xavier Standaert. A Systematic Approach to the Side-Channel Analysis of ECC Implementations with Worst-Case Horizontal Attacks. In Wieland Fischer and Naofumi Homma, editors, CHES 2017, volume 10529 of LNCS, pages 534–554. September 2017. Springer, Heidelberg. DOI: 10.1007/978-3-319-66787-4_26
[QLL18]
Jie-Lin Qiu, Wei Liu, and Bao-Liang Lu. Multi-view Emotion Recognition Using Deep Canonical Correlation Analysis. In Long Cheng, Andrew Chi-Sing Leung, and Seiichi Ozawa, editors, Neural Information Processing - 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part V, volume 11305 of Lecture Notes in Computer Science, pages 221–231. 2018. Springer. DOI: 10.1007/978-3-030-04221-9_20
[RMH17]
Ciara Rafferty, Máire McLoone, and Neil Hanley. Evaluation of Large Integer Multiplication Methods on Hardware. IEEE Trans. Computers, 66(8):1369–1382, 2017. DOI: 10.1109/TC.2017.2677426
[Rou87]
Peter J Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53–65, 1987.
[SHKS15]
Robert Specht, Johann Heyszl, Martin Kleinsteuber, and Georg Sigl. Improving Non-profiled Attacks on Exponentiations Based on Clustering and Extracting Leakage from Multi-channel High-Resolution EM Measurements. In Stefan Mangard and Axel Y. Poschmann:, editors, COSADE 2015, volume 9064 of LNCS, pages 3–19. April 2015. Springer, Heidelberg. DOI: 10.1007/978-3-319-21476-4_1
[SIUH22]
Kotaro Saito, Akira Ito, Rei Ueno, and Naofumi Homma. One Truth Prevails: A Deep-learning Based Single-Trace Power Analysis on RSA-CRT with Windowed Exponentiation. IACR Trans. Cryptogr. Hardw. Embed. Syst., 2022(4):490–526, 2022. DOI: 10.46586/TCHES.V2022.I4.490-526
[SKF+22]
Seiya Shimada, Kunihiro Kuroda, Yuta Fukuda, Kota Yoshida, and Takeshi Fujino. Deep Learning-Based Side-Channel Attacks against Software-Implemented RSA using Binary Exponentiation with Dummy Multiplication. In Hiroki Nishikawa and Xiangbo Kong, editors, Proceedings of the 4th International Symposium on Advanced Technologies and Applications in the Internet of Things (ATAIT 2022), Ibaraki and Virtual, Japan, August 24-26, 2022, volume 3198 of CEUR Workshop Proceedings, pages 75–84. 2022. CEUR-WS.org.
[SM23]
Marvin Staib and Amir Moradi. Deep Learning Side-Channel Collision Attack. IACR Trans. Cryptogr. Hardw. Embed. Syst., 2023(3):422–444, 2023. DOI: 10.46586/TCHES.V2023.I3.422-444
[SSS15]
Takeshi Sugawara, Daisuke Suzuki, and Minoru Saeki. Two Operands of Multipliers in Side-Channel Attack. In Stefan Mangard and Axel Y. Poschmann:, editors, COSADE 2015, volume 9064 of LNCS, pages 64–78. April 2015. Springer, Heidelberg. DOI: 10.1007/978-3-319-21476-4_5
[SSSL20]
Zhongkai Sun, Prathusha Kameswara Sarma, William A. Sethares, and Yingyu Liang. Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pages 8992–8999. 2020. AAAI Press. DOI: 10.1609/AAAI.V34I05.6431
[SXW+23]
Xiang-Jun Shen, Zhaorui Xu, Liangjun Wang, Zechao Li, Guangcan Liu, Jianping Fan, and ZhengJun Zha. Extraordinarily Time-and Memory-Efficient Large-Scale Canonical Correlation Analysis in Fourier Domain: From Shallow to Deep. IEEE Transactions on Neural Networks and Learning Systems, 2023. DOI: 10.1109/tnnls.2023.3282785
[Tim19]
Benjamin Timon. Non-Profiled Deep Learning-based Side-Channel attacks with Sensitivity Analysis. IACR TCHES, 2019(2):107–131, 2019. https://tches.iacr.org/index.php/TCHES/article/view/7387 DOI: 10.13154/tches.v2019.i2.107-131
[Wal01]
Colin D. Walter. Sliding Windows Succumbs to Big Mac Attack. In Çetin Kaya Koç, David Naccache, and Christof Paar, editors, CHES 2001, volume 2162 of LNCS, pages 286–299. May 2001. Springer, Heidelberg. DOI: 10.1007/3-540-44709-1_24
[WvM11]
Marc F. Witteman, Jasper G. J. van Woudenberg, and Federico Menarini. Defeating RSA Multiply-Always and Message Blinding Countermeasures. In Aggelos Kiayias, editor, CT-RSA 2011, volume 6558 of LNCS, pages 77–88. February 2011. Springer, Heidelberg. DOI: 10.1007/978-3-642-19074-2_6
[YM15]
Fei Yan and Krystian Mikolajczyk. Deep correlation for matching images and text. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015, pages 3441–3450. 2015. IEEE Computer Society. DOI: 10.1109/CVPR.2015.7298966
[ZBHV19]
Gabriel Zaid, Lilian Bossuet, Amaury Habrard, and Alexandre Venelli. Methodology for Efficient CNN Architectures in Profiling Attacks. IACR TCHES, 2020(1):1–36, 2019. https://tches.iacr.org/index.php/TCHES/article/view/8391 DOI: 10.13154/tches.v2020.i1.1-36
[ZBHV21]
Gabriel Zaid, Lilian Bossuet, Amaury Habrard, and Alexandre Venelli. Efficiency through Diversity in Ensemble Models applied to Side-Channel Attacks. IACR TCHES, 2021(3):60–96, 2021. https://tches.iacr.org/index.php/TCHES/article/view/8968 DOI: 10.46586/tches.v2021.i3.60-96
PDF
History
Submitted: 2025-01-13
Accepted: 2025-03-11
Published: 2025-04-08
Accepted: 2025-03-11
Published: 2025-04-08
How to cite
Dorian Llavata, Eleonora Cagli, Rémi Eyraud, Vincent Grosso, and Lilian Bossuet, Unsupervised Horizontal Attacks against Public-Key Primitives with DCCA. IACR Communications in Cryptology, vol. 2, no. 1, Apr 08, 2025, doi: 10.62056/ah5w7ta5v.
License
Copyright is held by the author(s)
This work is licensed under a Creative Commons Attribution (CC BY) license.