Study of Deep Learning Techniques for Side-Channel Analysis and Introduction to ASCAD Database

To provide insurance on the resistance of a system against side-channel analysis, several national or private schemes are today promoting an evaluation strategy, common in classical cryptography, which is focussing on the most powerful adversary who may train to learn about the dependency between the device behaviour and the sensitive data values. Several works have shown that this kind of analysis, known as Template Attacks in the side-channel domain, can be rephrased as a classical Machine Learning classification problem with learning phase. Following the current trend in the latter area, recent works have demonstrated that deep learning algorithms were very efficient to conduct security evaluations of embedded systems and had many advantage compared to the other methods. Unfortunately, their hyper-parametrization has often been kept secret by the authors who only discussed on the main design principles and on the attack efficiencies. This is clearly an important limitation of previous works since (1) the latter parametrization is known to be a challenging question in Machine Learning and (2) it does not allow for the reproducibility of the presented results. This paper aims to address theses limitations in several ways. First, completing recent works, we propose a comprehensive study of deep learning algorithms when applied in the context of side-channel analysis and we clarify the links with the classical template attacks. Secondly, we address the question of the choice of the hyper-parameters for the class of multi-layer perceptron networks and convolutional neural networks. Several benchmarks and rationales are given in the context of the analysis of a masked implementation of the AES algorithm. To enable perfect reproducibility of our tests, this work also introduces an open platform including all the sources of the target implementation together with the campaign of electro-magnetic measurements exploited in our benchmarks. This open database, named ASCAD, has been specified to serve as a common basis for further works on this subject. Our work confirms the conclusions made by Cagli et al. at CHES 2017 about the high potential of convolutional neural networks. Interestingly, it shows that the approach followed to design the algorithm VGG-16 used for image recognition seems also to be sound when it comes to fix an architecture for side-channel analysis

Analyzing the limits of deep learning applied to side channel attacks

Society is advancing by leaps and bounds in terms of technology in recent decades. These advances come with new products and services, which are generally designed within a few years, and potentially without undergoing tests to verify whether they are susceptible to physical or logical attacks. In an increasingly connected world, it is necessary to highlight the importance of cybersecurity. Within cybersecurity there is the field of hardware, where products can also have vulnerabilities. For instance, the information that cryptographic algorithms manage could be exploited by an attacker. This thesis is based on one of the most innovative techniques for analysing side-channel attacks: deep learning. In particular, the limits that may exist in the world of side-channel analysis techniques applying deep learning are explored, introducing the readers to the exciting world of hardware attacks. In addition, this thesis provides an introduction to neural computation. After gaining a detailed understanding of the functioning of ANN applied to SCA through the experiments carried out, the initial results have been improved by implementing two techniques proposed by researchers.La sociedad avanza a pasos agigantados en materia de tecnología en las últimas décadas. Estos avances vienen acompañados de nuevos productos y servicios, que generalmente se diseñan en pocos años, y potencialmente sin someterse a pruebas para verificar si son susceptibles de ataques físicos o lógicos. En un mundo cada vez más conectado, es necesario destacar la importancia de la ciberseguridad. Dentro de la ciberseguridad está el campo del hardware, donde los productos también pueden tener vulnerabilidades. Por ejemplo, la información que manejan los algoritmos criptográficos podría ser explotada por un atacante. Esta tesis se basa en una de las técnicas más innovadoras para analizar los ataques de canal lateral: deep learning. En particular, se exploran los límites que pueden existir en el mundo de las técnicas de análisis canal lateral aplicando aprendizaje profundo, introduciendo a los lectores en el apasionante mundo de los ataques por hardware. Además, esta tesis ofrece una introducción a la computación neuronal. Tras conocer en detalle el funcionamiento de la RNA aplicada a la SCA a través de los experimentos realizados, se han mejorado los resultados iniciales aplicando dos técnicas propuestas por los investigadores.La societat avança amb passes de gegant en matèria de tecnologia en les últimes dècades. Aquests avanços venen acompanyats de nous productes i serveis, que generalment es dissenyen en pocs anys, i potencialment sense sotmetre's a proves per a verificar si són susceptibles d'atacs físics o lògics. En un món cada vegada més connectat, és necessari destacar la importància de la ciberseguretat. Dins de la ciberseguretat està el camp del hardware, on els productes també poden tenir vulnerabilitats. Per exemple, la informació que manegen els algorismes criptogràfics podria ser explotada per un atacant. Aquesta tesi es basa en una de les tècniques més innovadores per a analitzar els atacs de canal lateral : deep learning. En particular, s'exploren els límits que poden existir en el món de les tècniques d'anàlisis de canal lateral aplicant l'aprenentatge profund, introduint als lectors en l'apassionant món dels atacs hardware. A més, aquesta tesi ofereix una introducció a la computació neuronal. Després de conèixer detalladament el funcionament de les ANN aplicades a SCA a través dels experiments realitzats, s'han millorat els resultats inicials aplicant dues tècniques proposades pels investigadors

Investigating Efficient Deep Learning Architectures For Side-Channel Attacks on AES

Over the past few years, deep learning has been getting progressively more popular for the exploitation of side-channel vulnerabilities in embedded cryptographic applications, as it offers advantages in terms of the amount of attack traces required for effective key recovery. A number of effective attacks using neural networks have already been published, but reducing their cost in terms of the amount of computing resources and data required is an ever-present goal, which we pursue in this work. We focus on the ANSSI Side-Channel Attack Database (ASCAD), and produce a JAX-based framework for deep-learning-based SCA, with which we reproduce a selection of previous results and build upon them in an attempt to improve their performance. We also investigate the effectiveness of various Transformer-based models.Comment: 12 pages, 6 figures. This manuscript is a report produced as part of a T\'el\'ecom Paris "PRIM" (Project Recherche et Innovation Master / Master's Research and Innovation Project

NASCTY: Neuroevolution to Attack Side-channel Leakages Yielding Convolutional Neural Networks

Side-channel analysis (SCA) can obtain information related to the secret key by exploiting leakages produced by the device. Researchers recently found that neural networks (NNs) can execute a powerful profiling SCA, even on targets protected with countermeasures. This paper explores the effectiveness of Neuroevolution to Attack Side-channel Traces Yielding Convolutional Neural Networks (NASCTY-CNNs), a novel genetic algorithm approach that applies genetic operators on architectures' hyperparameters to produce CNNs for side-channel analysis automatically. The results indicate that we can achieve performance close to state-of-the-art approaches on desynchronized leakages with mask protection, demonstrating that similar neuroevolution methods provide a solid venue for further research. Finally, the commonalities among the constructed NNs provide information on how NASCTY builds effective architectures and deals with the applied countermeasures.Comment: 19 pages, 6 figures, 4 table

Non-Profiled Side Channel Attack based on Deep Learning using Picture Trace

In this paper, we suggest a new format for converting side channel traces to fully utilize the deep learning schemes. Due to the fact that many deep learning schemes have been advanced based on MNIST style datasets, we convert from raw-trace based on float or byte data to picture-formatted trace based on position. This is induced that the best performance can be acquired from deep learning schemes. Although the overfitting cannot be avoided in our suggestion, the accuracy for validation outperforms to previous results of side channel analysis based on deep learning. Additionally, we provide a novel criteria for attack success or fail based on statistical confidence level rather than rule of thumb. Even though the data storage is slightly increased, our suggestion can completely be recovered the correct key compared to previous results. Moreover, our suggestion scheme has a lot of potential to improve side channel attack