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26 results on '"Picek, Stjepan"'

1. Towards Private Deep Learning-Based Side-Channel Analysis Using Homomorphic Encryption : Opportunities and Limitations

Author

Schmid, Fabian, Mukherjee, Shibam, Picek, Stjepan, Stöttinger, Marc, De Santis, Fabrizio, Rechberger, Christian, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wacquez, Romain, editor, and Homma, Naofumi, editor

Published
2024
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2. The Need for MORE: Unsupervised Side-Channel Analysis with Single Network Training and Multi-output Regression

Author

Savu, Ioana, Krček, Marina, Perin, Guilherme, Wu, Lichao, Picek, Stjepan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wacquez, Romain, editor, and Homma, Naofumi, editor

Published
2024
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3. PROACT - Physical Attack Resistance of Cryptographic Algorithms and Circuits with Reduced Time to Market

Author

Adhikary, Asmita, Basurto, Abraham, Batina, Lejla, Buhan, Ileana, Daemen, Joan, Mella, Silvia, Mentens, Nele, Picek, Stjepan, Ramachandran, Durga Lakshmi, Sajadi, Abolfazl, Stefanov, Todor, Vermoen, Dennis, Zidaric, Nusa, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Skliarova, Iouliia, editor, Brox Jiménez, Piedad, editor, Véstias, Mário, editor, and Diniz, Pedro C., editor

Published
2024
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7. On the Evaluation of Deep Learning-Based Side-Channel Analysis

Author

Wu, Lichao, Perin, Guilherme, Picek, Stjepan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Balasch, Josep, editor, and O’Flynn, Colin, editor

Published
2022
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8. Focus is Key to Success: A Focal Loss Function for Deep Learning-Based Side-Channel Analysis

Author

Kerkhof, Maikel, Wu, Lichao, Perin, Guilherme, Picek, Stjepan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Balasch, Josep, editor, and O’Flynn, Colin, editor

Published
2022
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9. Learning When to Stop: A Mutual Information Approach to Prevent Overfitting in Profiled Side-Channel Analysis

Author

Perin, Guilherme, Buhan, Ileana, Picek, Stjepan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bhasin, Shivam, editor, and De Santis, Fabrizio, editor

Published
2021
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10. On the Influence of Optimizers in Deep Learning-Based Side-Channel Analysis

Author

Perin, Guilherme, Picek, Stjepan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Dunkelman, Orr, editor, Jacobson, Jr., Michael J., editor, and O'Flynn, Colin, editor

Published
2021
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11. Kilroy Was Here: The First Step Towards Explainability of Neural Networks in Profiled Side-Channel Analysis

Author

van der Valk, Daan, Picek, Stjepan, Bhasin, Shivam, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bertoni, Guido Marco, editor, and Regazzoni, Francesco, editor

Published
2021
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13. On the Performance of Convolutional Neural Networks for Side-Channel Analysis

Author

Picek, Stjepan, Samiotis, Ioannis Petros, Kim, Jaehun, Heuser, Annelie, Bhasin, Shivam, Legay, Axel, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Chattopadhyay, Anupam, editor, Rebeiro, Chester, editor, and Yarom, Yuval, editor

Published
2018
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14. On the Construction of Side-Channel Attack Resilient S-boxes

Author

Lerman, Liran, Veshchikov, Nikita, Picek, Stjepan, Markowitch, Olivier, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, and Guilley, Sylvain, editor

Published
2017
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15. Resolving the Doubts: On the Construction and Use of ResNets for Side-Channel Analysis.

Author

Karayalcin, Sengim, Perin, Guilherme, and Picek, Stjepan

Subjects
CONVOLUTIONAL neural networks, DEEP learning, REINFORCEMENT learning, SCIENTIFIC community
Abstract

The deep learning-based side-channel analysis gave some of the most prominent side-channel attacks against protected targets in the past few years. To this end, the research community's focus has been on creating the following: (1) powerful multilayer perceptron or convolutional neural network architectures and (2) (if possible) minimal multilayer perceptron or convolutional neural network architectures. Currently, we see that, computationally intensive hyperparameter tuning methods (e.g., Bayesian optimization or reinforcement learning) provide the best results. However, as targets with more complex countermeasures become available, these minimal architectures may be insufficient, and we will require novel deep learning approaches.This work explores how residual neural networks (ResNets) perform in side-channel analysis and how to construct deeper ResNets capable of working with larger input sizes and requiring minimal tuning. The resulting architectures, obtained by following our guidelines, are significantly deeper than commonly seen in side-channel analysis, require minimal hyperparameter tuning for specific datasets, and offer competitive performance with state-of-the-art methods across several datasets. Additionally, the results indicate that ResNets work especially well when the number of profiling traces and features in a trace is large. [ABSTRACT FROM AUTHOR]

Published
2023
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16. On (in)Security of Edge-based Machine Learning Against Electromagnetic Side-channels

Author

Bhasin, Shivam, Jap, Dirmanto, Picek, Stjepan, 2022 IEEE International Symposium on Electromagnetic Compatibility & Signal/Power Integrity (EMCSI), and Temasek Laboratories

Subjects
Machine Learning, Electrical and electronic engineering::Computer hardware, software and systems [Engineering], Computer science and engineering::Computing methodologies::Artificial intelligence [Engineering], Side-Channel Analysis
Abstract

Machine (deep) learning represents mainstream re- search and development direction. This success can be linked to the ever-increasing computational capabilities and vast amounts of available data, resulting in ever more sophisticated machine learning models. The design and training of such machine learning models are challenging and expensive tasks, which incentivize companies to protect and keep it secret. Additionally, the wide applicability of machine learning results in diverse deployment scenarios. Many machine learning architectures are deployed on edge devices, such as sensors or actuators, making them susceptible to side-channel attacks. This work surveys the research works considering electromagnetic side-channel and edge-based machine learning models. After discussing state-of-the-art attacks and countermeasures, we propose several open problems to be investigated in future research. National Research Foundation (NRF) Submitted/Accepted version This research is supported in parts by the National Research Foundation, Singapore, under its National Cybersecurity Research & Development Programme/Cyber-Hardware Forensic & Assurance Evaluation R&D Programme (Award: NRF2018NCR-NCR009-0001). This work received funding in the framework of the NWA Cybersecurity Call with project name PROACT with project number NWA.1215.18.014, which is (partly) financed by the Netherlands Organisation for Scientific Research (NWO). Additionally, this work was supported in part by the Netherlands Organization for Scientific Research NWO project DISTANT (CS.019).

Published
2022

17. Reinforcement Learning-Based Design of Side-Channel Countermeasures

Author

Rijsdijk, Jorai, Wu, L., Perin, G., Batina, Lejla, Picek, Stjepan, and Mondal, Mainack

Subjects
Countermeasures, Side-channel analysis, Reinforcement learning, Deep learning
Abstract

Deep learning-based side-channel attacks are capable of breaking targets protected with countermeasures. The constant progress in the last few years makes the attacks more powerful, requiring fewer traces to break a target. Unfortunately, to protect against such attacks, we still rely solely on methods developed to protect against generic attacks. The works considering the protection perspective are few and usually based on the adversarial examples concepts, which are not always easy to translate to real-world hardware implementations. In this work, we ask whether we can develop combinations of countermeasures that protect against side-channel attacks. We consider several widely adopted hiding countermeasures and use the reinforcement learning paradigm to design specific countermeasures that show resilience against deep learning-based side-channel attacks. Our results show that it is possible to significantly enhance the target resilience to a point where deep learning-based attacks cannot obtain secret information. At the same time, we consider the cost of implementing such countermeasures to balance security and implementation costs. The optimal countermeasure combinations can serve as development guidelines for real-world hardware/software-based protection schemes.

Published
2022

18. On the Importance of Pooling Layer Tuning for Profiling Side-Channel Analysis

Author

Wu, L., Perin, G., Zhou, Jianying, Ahmed, Chuadhry Mujeeb, Batina, Lejla, Chattopadhyay, Sudipta, Gadyatskaya, Olga, Jin, Chenglu, Lin, Jingqiang, Losiouk, Eleonora, Luo, Bo, Majumdar, Suryadipta, Maniatakos, Mihalis, Mashima, Daisuke, Meng, Weizhi, Picek, Stjepan, Shimaoka, Masaki, Su, Chunhua, and Wang, Cong

Subjects
Profiling (computer programming), Pooling, Artificial neural network, Computer science, business.industry, Side-channel analysis, Deep learning, Dimensionality reduction, computer.software_genre, Convolutional neural network, Convolutional neural networks, Data mining, Side channel attack, Artificial intelligence, business, computer, TRACE (psycholinguistics)
Abstract

In recent years, the advent of deep neural networks opened new perspectives for security evaluations with side-channel analysis. Profiling attacks now benefit from capabilities offered by convolutional neural networks, such as dimensionality reduction and the inherent ability to reduce the trace desynchronization effects. These neural networks contain at least three types of layers: convolutional, pooling, and dense layers. Although the definition of pooling layers causes a large impact on neural network performance, a study on pooling hyperparameters effect on side-channel analysis is still not provided in the academic community. This paper provides extensive experimental results to demonstrate how pooling layer types and pooling stride and size affect the profiling attack performance with convolutional neural networks. Additionally, we demonstrate that pooling hyperparameters can be larger than usually used in related works and still keep good performance for profiling attacks on specific datasets.

Published
2021

19. Lightweight Ciphers and Their Side-Channel Resilience.

Author

Heuser, Annelie, Picek, Stjepan, Guilley, Sylvain, and Mentens, Nele

Subjects
*CIPHERS, *STREAM ciphers, *LIGHT emitting diodes
Abstract

Side-channel attacks represent a powerful category of attacks against cryptographic devices. Still, side-channel analysis for lightweight ciphers is much less investigated than for instance for AES. Although intuition may lead to the conclusion that lightweight ciphers are weaker in terms of side-channel resistance, that remains to be confirmed and quantified. In this paper, we consider various side-channel analysis metrics which should provide an insight on the resistance of lightweight ciphers against side-channel attacks. In particular, for the non-profiled scenario we use the theoretical confusion coefficient and empirical optimal distinguisher. Our study considers side-channel attacks on the first, the last, or both rounds simultaneously. Furthermore, we conduct a profiled side-channel analysis using various machine learning attacks to recover 4-bit and 8-bit intermediate states of the cipher. Our results show that the difference between AES and lightweight ciphers is smaller than one would expect, and even find scenarios in which lightweight ciphers may be more resistant. Interestingly, we observe that the studied 4-bit S-boxes have a different side-channel resilience, while the difference in the 8-bit ones is only theoretically present. [ABSTRACT FROM AUTHOR]

Published
2020
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20. Practical Aspects of Vertical Side-Channel Analyses on HMAC-SHA-2

Author

Vlasak, Lukas, Bouvet, Antoine, Guilley, Sylvain, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Chattopadhyay, Anupam, editor, Bhasin, Shivam, editor, Picek, Stjepan, editor, and Rebeiro, Chester, editor

Published
2024
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21. A Deep-Learning Approach for Predicting Round Obfuscation in White-Box Block Ciphers

Author

Deng, Tongxia, Li, Ping, Yang, Shunzhi, Zhang, Yupeng, Gong, Zheng, Duan, Ming, Luo, Yiyuan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhou, Jianying, editor, Batina, Lejla, editor, Li, Zengpeng, editor, Lin, Jingqiang, editor, Losiouk, Eleonora, editor, Majumdar, Suryadipta, editor, Mashima, Daisuke, editor, Meng, Weizhi, editor, Picek, Stjepan, editor, Rahman, Mohammad Ashiqur, editor, Shao, Jun, editor, Shimaoka, Masaki, editor, Soremekun, Ezekiel, editor, Su, Chunhua, editor, Teh, Je Sen, editor, Udovenko, Aleksei, editor, Wang, Cong, editor, Zhang, Leo, editor, and Zhauniarovich, Yury, editor

Published
2023
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22. HWGN: Side-Channel Protected NNs Through Secure and Private Function Evaluation

Author

Hashemi, Mohammad, Roy, Steffi, Forte, Domenic, Ganji, Fatemeh, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Batina, Lejla, editor, Picek, Stjepan, editor, and Mondal, Mainack, editor

Published
2022
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23. Protecting the Most Significant Bits in Scalar Multiplication Algorithms

Author

Alpirez Bock, Estuardo, Chmielewski, Lukasz, Miteloudi, Konstantina, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Batina, Lejla, editor, Picek, Stjepan, editor, and Mondal, Mainack, editor

Published
2022
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24. On Reverse Engineering Neural Network Implementation on GPU

Author

Chmielewski, Łukasz, Weissbart, Léo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhou, Jianying, editor, Ahmed, Chuadhry Mujeeb, editor, Batina, Lejla, editor, Chattopadhyay, Sudipta, editor, Gadyatskaya, Olga, editor, Jin, Chenglu, editor, Lin, Jingqiang, editor, Losiouk, Eleonora, editor, Luo, Bo, editor, Majumdar, Suryadipta, editor, Maniatakos, Mihalis, editor, Mashima, Daisuke, editor, Meng, Weizhi, editor, Picek, Stjepan, editor, Shimaoka, Masaki, editor, Su, Chunhua, editor, and Wang, Cong, editor

Published
2021
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25. A Comparison of Weight Initializers in Deep Learning-Based Side-Channel Analysis

Author

Li, Huimin, Krček, Marina, Perin, Guilherme, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhou, Jianying, editor, Conti, Mauro, editor, Ahmed, Chuadhry Mujeeb, editor, Au, Man Ho, editor, Batina, Lejla, editor, Li, Zhou, editor, Lin, Jingqiang, editor, Losiouk, Eleonora, editor, Luo, Bo, editor, Majumdar, Suryadipta, editor, Meng, Weizhi, editor, Ochoa, Martín, editor, Picek, Stjepan, editor, Portokalidis, Georgios, editor, Wang, Cong, editor, and Zhang, Kehuan, editor

Published
2020
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26. The Forgotten Hyperparameter : Introducing Dilated Convolution for Boosting CNN-Based Side-Channel Attacks

Author

Paguada, Servio, Armendariz, Igor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhou, Jianying, editor, Conti, Mauro, editor, Ahmed, Chuadhry Mujeeb, editor, Au, Man Ho, editor, Batina, Lejla, editor, Li, Zhou, editor, Lin, Jingqiang, editor, Losiouk, Eleonora, editor, Luo, Bo, editor, Majumdar, Suryadipta, editor, Meng, Weizhi, editor, Ochoa, Martín, editor, Picek, Stjepan, editor, Portokalidis, Georgios, editor, Wang, Cong, editor, and Zhang, Kehuan, editor

Published
2020
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