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Your search keyword '"Picek, Stjepan"' showing total 31 results
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31 results on '"Picek, Stjepan"'

7. 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

13. 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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14. A Systematic Evaluation of Profiling Through Focused Feature Selection.

Author

Picek, Stjepan, Heuser, Annelie, Jovic, Alan, and Batina, Lejla

Subjects
FEATURE selection, SUPPORT vector machines, MACHINE learning
Abstract

Profiled side-channel attacks consist of several steps one needs to take. An important, but sometimes ignored, step is a selection of the points of interest (features) within side-channel measurement traces. A large majority of the related works start the analyses with an assumption that the features are preselected. Contrary to this assumption, here, we concentrate on the feature selection step. We investigate how advanced feature selection techniques stemming from the machine learning domain can be used to improve the attack efficiency. To this end, we provide a systematic evaluation of the methods of interest. The experiments are performed on several real-world data sets containing software and hardware implementations of AES, including the random delay countermeasure. Our results show that wrapper and hybrid feature selection methods perform extremely well over a wide range of test scenarios and a number of features selected. We emphasize L1 regularization (wrapper approach) and linear support vector machine (SVM) with recursive feature elimination used after chi-square filter (Hybrid approach) that performs well in both accuracy and guessing entropy. Finally, we show that the use of appropriate feature selection techniques is more important for an attack on the high-noise data sets, including those with countermeasures, than on the low-noise ones. [ABSTRACT FROM AUTHOR]

Published
2019
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25. Challenges in designing trustworthy cryptographic co-processors

Author

Graves, Ricardo, primary, Di Natale, Giorgio, additional, Batina, Lejla, additional, Bhasin, Shivam, additional, Ege, Baris, additional, Fournaris, Apostolos, additional, Mentens, Nele, additional, Picek, Stjepan, additional, Regazzoni, Francesco, additional, Rozic, Vladimir, additional, Sklavos, Nicolas, additional, and Yang, Bohan, additional

Published
2015
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31. On the analysis of experimental results in evolutionary computation.

Author

Picek, Stjepan, Golub, Marin, and Jakobovic, Domagoj

Abstract

Evolutionary computation methods are successfully applied in solving of combinatorial optimization problems. Since the “No Free Lunch” theorem states that there is no single best algorithm to solve all possible problems, throughout the years many algorithms and their modifications have emerged. When a new algorithm is developed, one question that naturally arises is how it compares to other algorithms, whether for some specific problem or in general performance. Because of the stochastic nature of systems involved, usually the only possible way of deriving the answer is to perform extensive experimental analysis. In this paper we provide an overview of possible approaches in the experimental analysis, and describe statistical methods that could be used. Furthermore, we outline similarities and differences between these methods, which lead to a discussion of important issues that need to be resolved when using these methods. [ABSTRACT FROM PUBLISHER]

Published
2012

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