Searched for: author%3A%22Perin%2C+G.%22
(1 - 20 of 20)
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Wu, L. (author), Won, Yoo-Seung (author), Jap, Dirmanto (author), Perin, G. (author), Bhasin, Shivam (author), Picek, S. (author)
The use of deep learning-based side-channel analysis is an effective way of performing profiling attacks on power and electromagnetic leakages, even against targets protected with countermeasures. While many research articles have reported successful results, they typically focus on profiling and attacking a single device, assuming that...
journal article 2024
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Picek, S. (author), Perin, G. (author), Mariot, L. (author), Wu, L. (author), Batina, Lejla (author)
Side-channel attacks represent a realistic and serious threat to the security of embedded devices for already almost three decades. A variety of attacks and targets they can be applied to have been introduced, and while the area of side-channel attacks and their mitigation is very well-researched, it is yet to be consolidated. Deep learning...
journal article 2023
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Kerkhof, Maikel (author), Wu, L. (author), Perin, G. (author), Picek, S. (author)
Deep learning is a powerful direction for profiling side-channel analysis as it can break targets protected with countermeasures even with a relatively small number of attack traces. Still, it is necessary to conduct hyperparameter tuning to reach strong attack performance, which can be far from trivial. Besides many options stemming from the...
journal article 2023
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Wu, L. (author), Weissbart, L.J.A. (author), Krcek, M. (author), Li, H. (author), Perin, G. (author), Batina, Lejla (author), Picek, S. (author)
The efficiency of the profiling side-channel analysis can be significantly improved with machine learning techniques. Although powerful, a fundamental machine learning limitation of being data-hungry received little attention in the side-channel community. In practice, the maximum number of leakage traces that evaluators/attackers can obtain is...
journal article 2023
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Krcek, M. (author), Perin, G. (author)
Hyperparameter tuning represents one of the main challenges in deep learning-based profiling side-channel analysis. For each different side-channel dataset, the typical procedure to find a profiling model is applying hyperparameter tuning from scratch. The main reason is that side-channel measurements from various targets contain different...
journal article 2023
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Perin, G. (author), Wu, L. (author), Picek, S. (author)
The adoption of deep neural networks for profiling side-channel attacks opened new perspectives for leakage detection. Recent publications showed that cryptographic implementations featuring different countermeasures could be broken without feature selection or trace preprocessing. This success comes with a high price: an extensive...
journal article 2023
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Wu, L. (author), Perin, G. (author), Picek, S. (author)
In the last decade, machine learning-based side-channel attacks have become a standard option when investigating profiling side-channel attacks. At the same time, the previous state-of-the-art technique, template attack, started losing its importance and was more considered a baseline to compare against. As such, most of the results reported...
journal article 2022
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Perin, G. (author), Wu, L. (author), Picek, S. (author)
One of the main promoted advantages of deep learning in profiling side-channel analysis is the possibility of skipping the feature engineering process. Despite that, most recent publications consider feature selection as the attacked interval from the side-channel measurements is pre-selected. This is similar to the worst-case security...
journal article 2022
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Perin, G. (author), Wu, L. (author), Picek, S. (author)
Deep learning-based side-channel analysis (SCA) represents a strong approach for profiling attacks. Still, this does not mean it is trivial to find neural networks that perform well for any setting. Based on the developed neural network architectures, we can distinguish between small neural networks that are easier to tune and less prone to...
book chapter 2022
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Swaminathan, Sudharshan (author), Chmielewski, Łukasz (author), Perin, G. (author), Picek, S. (author)
Side-channel attacks (SCA) focus on vulnerabilities caused by insecure implementations and exploit them to deduce useful information about the data being processed or the data itself through leakages obtained from the device. There have been many studies exploiting these leakages, and most of the state-of-the-art attacks have been shown to...
conference paper 2022
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Picek, S. (author), Heuser, Annelie (author), Perin, G. (author), Guilley, Sylvain (author)
Profiled side-channel attacks represent the most powerful category of side-channel attacks. There, the attacker has access to a clone device to profile its leaking behavior. Additionally, it is common to consider the attacker unbounded in power to allow the worst-case security analysis. This paper starts with a different premise where we are...
conference paper 2022
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Wu, L. (author), Perin, G. (author), Picek, S. (author)
Deep learning-based side-channel analysis is rapidly positioning itself as a de-facto standard for the most powerful profiling side-channel analysis.The results from the last few years show that deep learning techniques can efficiently break targets that are even protected with countermeasures. While there are constant improvements in making...
conference paper 2022
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Kerkhof, Maikel (author), Wu, L. (author), Perin, G. (author), Picek, S. (author)
The deep learning-based side-channel analysis represents one of the most powerful side-channel attack approaches. Thanks to its capability in dealing with raw features and countermeasures, it becomes the de facto standard approach for the SCA community. The recent works significantly improved the deep learning-based attacks from various...
conference paper 2022
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Rezaeezade, A. (author), Perin, G. (author), Picek, S. (author)
Profiling side-channel analysis allows evaluators to estimate the worst-case security of a target. When security evaluations relax the assumptions about the adversary’s knowledge, profiling models may easily be sub-optimal due to the inability to extract the most informative points of interest from the side-channel measurements. When used for...
conference paper 2022
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Arora, Vipul (author), Buhan, Ileana (author), Perin, G. (author), Picek, S. (author)
Advances in cryptography have enabled the features of confidentiality, security, and integrity on small embedded devices such as IoT devices. While mathematically strong, the platform on which an algorithm is implemented plays a significant role in the security of the final product. Side-channel attacks exploit the variations in the system’s...
conference paper 2022
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Rijsdijk, Jorai (author), Wu, L. (author), Perin, G. (author)
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...
conference paper 2022
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Rijsdijk, J. (author), Wu, L. (author), Perin, G. (author), Picek, S. (author)
Deep learning represents a powerful set of techniques for profiling side-channel analysis. The results in the last few years show that neural network architectures like multilayer perceptron and convolutional neural networks give strong attack performance where it is possible to break targets protected with various coun-termeasures....
journal article 2021
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Wu, L. (author), Perin, G. (author)
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...
conference paper 2021
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Perin, G. (author), Chmielewski, Łukasz (author), Picek, S. (author)
The adoption of deep neural networks for profiled side-channel attacks provides powerful options for leakage detection and key retrieval of secure products. When training a neural network for side-channel analysis, it is expected that the trained model can implement an approximation function that can detect leaking side-channel samples and, at...
journal article 2020
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Perin, G. (author), Chmielewski, Łukasz (author), Batina, Lejla (author), Picek, S. (author)
To mitigate side-channel attacks, real-world implementations of public-key cryptosystems adopt state-of-the-art countermeasures based on randomization of the private or ephemeral keys. Usually, for each private key operation, a “scalar blinding” is performed using 32 or 64 randomly generated bits. Nevertheless, horizontal attacks based on a...
journal article 2020
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