| Reinforcement learning for hyperparameter tuning in deep learning-based side-channel analysis J Rijsdijk, L Wu, G Perin, S Picek IACR Transactions on Cryptographic Hardware and Embedded Systems 2021 (3 …, 2021 | 141 | 2021 |
| Sok: Deep learning-based physical side-channel analysis S Picek, G Perin, L Mariot, L Wu, L Batina ACM Computing Surveys 55 (11), 1-35, 2023 | 121 | 2023 |
| I choose you: Automated hyperparameter tuning for deep learning-based side-channel analysis L Wu, G Perin, S Picek IEEE Transactions on Emerging Topics in Computing, 2022 | 111 | 2022 |
| Strength in numbers: Improving generalization with ensembles in machine learning-based profiled side-channel analysis G Perin, Ł Chmielewski, S Picek IACR Transactions on Cryptographic Hardware and Embedded Systems, 337-364, 2020 | 105 | 2020 |
| Attacking randomized exponentiations using unsupervised learning G Perin, L Imbert, L Torres, P Maurine Constructive Side-Channel Analysis and Secure Design: 5th International …, 2014 | 65 | 2014 |
| Exploring feature selection scenarios for deep learning-based side-channel analysis G Perin, L Wu, S Picek IACR Transactions on Cryptographic Hardware and Embedded Systems 2022 (4 …, 2022 | 63 | 2022 |
| A comparison of weight initializers in deep learning-based side-channel analysis H Li, M Krček, G Perin Applied Cryptography and Network Security Workshops: ACNS 2020 Satellite …, 2020 | 58 | 2020 |
| The best of two worlds: Deep learning-assisted template attack L Wu, G Perin, S Picek IACR Transactions on Cryptographic Hardware and Embedded Systems 2022 (3 …, 2022 | 47 | 2022 |
| Keep it unsupervised: Horizontal attacks meet deep learning G Perin, Ł Chmielewski, L Batina, S Picek IACR Transactions on Cryptographic Hardware and Embedded Systems, 343-372, 2021 | 47 | 2021 |
| Profiled side-channel analysis in the efficient attacker framework S Picek, A Heuser, G Perin, S Guilley International Conference on Smart Card Research and Advanced Applications, 44-63, 2021 | 44 | 2021 |
| Learning when to stop: A mutual information approach to prevent overfitting in profiled side-channel analysis G Perin, I Buhan, S Picek International Workshop on Constructive Side-Channel Analysis and Secure …, 2021 | 43 | 2021 |
| On the influence of optimizers in deep learning-based side-channel analysis G Perin, S Picek Selected Areas in Cryptography: 27th International Conference, Halifax, NS …, 2021 | 41 | 2021 |
| A semi-parametric approach for side-channel attacks on protected RSA implementations G Perin, Ł Chmielewski International Conference on Smart Card Research and Advanced Applications, 34-53, 2015 | 35 | 2015 |
| Montgomery modular multiplication on reconfigurable hardware: fully systolic array vs parallel implementation G Perin, DG Mesquita, FL Herrmann, JB Martins 2010 VI Southern Programmable Logic Conference (SPL), 61-66, 2010 | 33 | 2010 |
| Montgomery modular multiplication on reconfigurable hardware: Systolic versus multiplexed implementation G Perin, D Gomes Mesquita, JB Martins International Journal of Reconfigurable Computing 2011 (1), 127147, 2011 | 30 | 2011 |
| A gigabit udp/ip network stack in fpga FL Herrmann, G Perin, JPJ de Freitas, R Bertagnolli, ... 2009 16th IEEE International Conference on Electronics, Circuits and Systems …, 2009 | 26 | 2009 |
| No (good) loss no gain: systematic evaluation of loss functions in deep learning-based side-channel analysis M Kerkhof, L Wu, G Perin, S Picek Journal of Cryptographic Engineering 13 (3), 311-324, 2023 | 24 | 2023 |
| Neural network model assessment for side-channel analysis G Perin, B Ege, L Chmielewski Cryptology ePrint Archive, 2019 | 24 | 2019 |
| On the attack evaluation and the generalization ability in profiling side-channel analysis L Wu, L Weissbart, M Krček, H Li, G Perin, L Batina, S Picek Cryptology ePrint Archive, 2020 | 23 | 2020 |
| Lowering the bar: Deep learning for side-channel analysis (white-paper) G Perin, B Ege, J van Woudenberg Proc. BlackHat, 1-15, 2018 | 20 | 2018 |
Stjepan PicekAssociate professor, Radboud University, The NetherlandsVerified email at computer.org
