A Key to Success -- Success Exponents for Side-Channel Distinguishers

The success rate is the classical metric for evaluating the performance of side-channel attacks. It is generally computed empirically from measurements for a particular device or using simulations. Closed-form expressions of success rate are desirable because they provide an explicit functional dependence on relevant parameters such as number of measurements and signal-to-noise ratio which help to understand the effectiveness of a given attack and how one can mitigate its threat by countermeasures. However, such closed-form expressions involve high-dimensional complex statistical functions that are hard to estimate. In this paper, we define the success exponent (SE) of an arbitrary side-channel distinguisher as the first-order exponent of the success rate as the number of measurements increases. Under fairly general assumptions such as soundness, we give a general simple formula for any arbitrary distinguisher and derive closed-form expressions of it for DoM, CPA, MIA and the optimal distinguisher when the model is known (template attack). For DoM and CPA our results are in line with the literature. Experiments confirm that the theoretical closed-form expression of the SE coincides with the empirically computed one, even for reasonably small numbers of measurements. Finally, we highlight that our study raises many new perspectives for comparing and evaluating side-channel attacks, countermeasures and implementations

One for All, All for One: A Unified Evaluation Framework for Univariate DPA Attacks

Success Rate (SR) is empirically and theoretically a common metric for evaluating the performance of side-channel attacks. Intuitive expressions of success rate are desirable since they reveal and explain the functional dependence on relevant parameters, such as number of measurements and Signal-to-Noise Ratio (SNR), in a straightforward manner. Meanwhile, existing works more or less expose unsolved fundamental problems, such as strong leakage assumption, difficulty in interpretation of principle, inaccurate evaluation, and inconsideration of high-order SR. In this paper, we first provide an intuitive framework that statistical tests embedded in different univariate DPA attacks are unified as analyzing and comparing visualized vectors in a Euclidean space by using different easy-to-understand metrics. Then, we establish a unified framework to abstract and convert the security evaluations to the problem of finding a boundary in the Euclidean space. With expressions of the boundary, judging whether a DPA attack succeeds in sense of $o^{th}$-order becomes fairly efficient and intuitive, and the corresponding SR can be calculated theoretically by integral. Finally, we propose an algorithm that is capable of estimating arbitrary order of SR effectively. Our experimental results verify the theory and highlight the superiority. We believe our research raises many new perspectives for comparing and evaluating side-channel attacks, countermeasures and implementations

Best Information is Most Successful

Using information-theoretic tools, this paper establishes a mathematical link between the probability of success of a side-channel attack and the minimum number of queries to reach a given success rate, valid for any possible distinguishing rule and with the best possible knowledge on the attacker\u27s side. This link is a lower bound on the number of queries highly depends on Shannon\u27s mutual information between the traces and the secret key. This leads us to derive upper bounds on the mutual information that are as tight as possible and can be easily calculated. It turns out that, in the case of an additive white Gaussian noise, the bound on the probability of success of any attack is directly related to the signal to noise ratio. This leads to very easy computations and predictions of the success rate in any leakage model

Snowball: Another View on Side-Channel Key Recovery Tools

The performance of Side-Channel Attacks (SCAs) decays rapidly when considering more sub-keys, making the full-key recovery a very challenging problem. Limited to independent collision information utilization, collision attacks establish the relationship among sub-keys but do not significantly slow down this trend. To solve it, we first exploit the samples from the previously attacked S-boxes to assist attacks on the targeted S-box under an assumption that similar leakage occurs in program loop or code reuse scenarios. The later considered S-boxes are easier to be recovered since more samples participate in this assist attack, which results in the ``snowball\u27\u27 effect. We name this scheme as Snowball, which significantly slows down the attenuation rate of attack performance. We further introduce confusion coefficient into the collision attack to construct collision confusion coefficient, and deduce its relationship with correlation coefficient. Based on this relationship, we give two optimizations on our Snowball exploiting the ``values\u27\u27 information and ``rankings\u27\u27 information of collision correlation coefficients named Least Deviation from Pearson correlation coefficient (PLD) and Least Deviation from confusion coefficient (CLD). Experiments show that the above optimizations significantly improve the performance of our Snowball

Ten years of cube attacks

In 2009, Dinur and Shamir proposed the cube attack, an algebraic cryptanalysis technique that only requires black box access to a target cipher. Since then, this attack has received both many criticisms and endorsements from crypto community; this work aims at revising and collecting the many attacks that have been proposed starting from it. We categorise all of these attacks in five classes; for each class, we provide a brief summary description along with the state-of-the-art references and the most recent cryptanalysis results. Furthermore, we extend and refine the new notation we proposed in 2021 and we use it to provide a consistent definition for each attack family. Finally, in the appendix, we provide an in-depth description of the kite attack framework, a cipher independent tool we firstly proposed in 2018 that implements the kite attack on GPUs. To prove its effectiveness, we use Mickey2.0 as a use case, showing how to embed it in the framework

Profiling Side-channel Analysis in the Efficient Attacker Framework

Profiling side-channel attacks represent the most powerful category of side-channel attacks. There, we assume that the attacker has access to a clone device to profile its leaking behavior. Additionally, we consider the attacker to be unbounded in power to give the worst-case security analysis. In this paper, we start with a different premise where we are interested in the minimum strength that the attacker requires to conduct a successful attack. To that end, we propose a new framework for profiling side-channel analysis that we call the Efficient Attacker Framework. With it, we require the attackers to use as powerful attacks as possible, but we also provide a setting that inherently allows a more objective analysis among attacks. We discuss the ramifications of having the attacker with unlimited power when considering the neural network-based attacks. There, we show that the Universal Approximation Theorem can be connected with neural network-based attacks able to break implementations with only a single measurement. Those considerations further strengthen the need for the Efficient Attacker Framework. To confirm our theoretical results, we provide an experimental evaluation of our framework

Systematic Characterization of Power Side Channel Attacks for Residual and Added Vulnerabilities

Power Side Channel Attacks have continued to be a major threat to cryptographic devices. Hence, it will be useful for designers of cryptographic systems to systematically identify which type of power Side Channel Attacks their designs remain vulnerable to after implementation. It’s also useful to determine which additional vulnerabilities they have exposed their devices to, after the implementation of a countermeasure or a feature. The goal of this research is to develop a characterization of power side channel attacks on different encryption algorithms\u27 implementations to create metrics and methods to evaluate their residual vulnerabilities and added vulnerabilities. This research studies the characteristics that influence the power side leakage, classifies them, and identifies both the residual vulnerabilities and the added vulnerabilities. Residual vulnerabilities are defined as the traits that leave the implementation of the algorithm still vulnerable to power Side Channel Attacks (SCA), sometimes despite the attempt at implementing countermeasures by the designers. Added vulnerabilities to power SCA are defined as vulnerabilities created or enhanced by the algorithm implementations and/or modifications. The three buckets in which we categorize the encryption algorithm implementations are: i. Countermeasures against power side channel attacks, ii. IC power delivery network impact to power leakage (including voltage regulators), iii. Lightweight ciphers and applications for the Internet of Things (IoT ) From the characterization of masking countermeasures, an example outcome developed is that masking schemes, when uniformly distributed random masks are used, are still vulnerable to collision power attacks. Another example outcome derived is that masked AES, when glitches occur, is still vulnerable to Differential Power Analysis (DPA). We have developed a characterization of power side-channel attacks on the hardware implementations of different symmetric encryption algorithms to provide a detailed analysis of the effectiveness of state-of-the-art countermeasures against local and remote power side-channel attacks. The characterization is accomplished by studying the attributes that influence power side-channel leaks, classifying them, and identifying both residual vulnerabilities and added vulnerabilities. The evaluated countermeasures include masking, hiding, and power delivery network scrambling. But, vulnerability to DPA depends largely on the quality of the leaked power, which is impacted by the characteristics of the device power delivery network. Countermeasures and deterrents to power side-channel attacks targeting the alteration or scrambling of the power delivery network have been shown to be effective against local attacks where the malicious agent has physical access to the target system. However, remote attacks that capture the leaked information from within the IC power grid are shown herein to be nonetheless effective at uncovering the secret key in the presence of these countermeasures/deterrents. Theoretical studies and experimental analysis are carried out to define and quantify the impact of integrated voltage regulators, voltage noise injection, and integration of on-package decoupling capacitors for both remote and local attacks. An outcome yielded by the studies is that the use of an integrated voltage regulator as a countermeasure is effective for a local attack. However, remote attacks are still effective and hence break the integrated voltage regulator countermeasure. From experimental analysis, it is observed that within the range of designs\u27 practical values, the adoption of on-package decoupling capacitors provides only a 1.3x increase in the minimum number of traces required to discover the secret key. However, the injection of noise in the IC power delivery network yields a 37x increase in the minimum number of traces to discover. Thus, increasing the number of on-package decoupling capacitors or the impedance between the local probing site and the IC power grid should not be relied on as countermeasures to power side-channel attacks, for remote attack schemes. Noise injection should be considered as it is more effective at scrambling the leaked signal to eliminate sensitive identifying information. However, the analysis and experiments carried out herein are applied to regular symmetric ciphers which are not suitable for protecting Internet of Things (IoT) devices. The protection of communications between IoT devices is of great concern because the information exchanged contains vital sensitive data. Malicious agents seek to exploit those data to extract secret information about the owners or the system. Power side channel attacks are of great concern on these devices because their power consumption unintentionally leaks information correlatable to the device\u27s secret data. Several studies have demonstrated the effectiveness of authenticated encryption with advanced data (AEAD), in protecting communications with these devices. In this research, we have proposed a comprehensive evaluation of the ten algorithm finalists of the National Institute of Standards and Technology (NIST) IoT lightweight cipher competition. The study shows that, nonetheless, some still present some residual vulnerabilities to power side channel attacks (SCA). For five ciphers, we propose an attack methodology as well as the leakage function needed to perform correlation power analysis (CPA). We assert that Ascon, Sparkle, and PHOTON-Beetle security vulnerability can generally be assessed with the security assumptions Chosen ciphertext attack and leakage in encryption only, with nonce-misuse resilience adversary (CCAmL1) and Chosen ciphertext attack and leakage in encryption only with nonce-respecting adversary (CCAL1) , respectively. However, the security vulnerability of GIFT-COFB, Grain, Romulus, and TinyJambu can be evaluated more straightforwardly with publicly available leakage models and solvers. They can also be assessed simply by increasing the number of traces collected to launch the attack

CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information

Machine learning has become mainstream across industries. Numerous examples proved the validity of it for security applications. In this work, we investigate how to reverse engineer a neural network by using only power side-channel information. To this end, we consider a multilayer perceptron as the machine learning architecture of choice and assume a non-invasive and eavesdropping attacker capable of measuring only passive side-channel leakages like power consumption, electromagnetic radiation, and reaction time. We conduct all experiments on real data and common neural net architectures in order to properly assess the applicability and extendability of those attacks. Practical results are shown on an ARM CORTEX-M3 microcontroller. Our experiments show that the side-channel attacker is capable of obtaining the following information: the activation functions used in the architecture, the number of layers and neurons in the layers, the number of output classes, and weights in the neural network. Thus, the attacker can effectively reverse engineer the network using side-channel information. Next, we show that once the attacker has the knowledge about the neural network architecture, he/she could also recover the inputs to the network with only a single-shot measurement. Finally, we discuss several mitigations one could use to thwart such attacks.Comment: 15 pages, 16 figure

Algorithmes quantiques pour la cryptanalyse et cryptographie symétrique post-quantique

Modern cryptography relies on the notion of computational security. The level of security given by a cryptosystem is expressed as an amount of computational resources required to break it. The goal of cryptanalysis is to find attacks, that is, algorithms with lower complexities than the conjectural bounds.With the advent of quantum computing devices, these levels of security have to be updated to take a whole new notion of algorithms into account. At the same time, cryptography is becoming widely used in small devices (smart cards, sensors), with new cost constraints.In this thesis, we study the security of secret-key cryptosystems against quantum adversaries.We first build new quantum algorithms for k-list (k-XOR or k-SUM) problems, by composing exhaustive search procedures. Next, we present dedicated cryptanalysis results, starting with a new quantum cryptanalysis tool, the offline Simon's algorithm. We describe new attacks against the lightweight algorithms Spook and Gimli and we perform the first quantum security analysis of the standard cipher AES.Finally, we specify Saturnin, a family of lightweight cryptosystems oriented towards post-quantum security. Thanks to a very similar structure, its security relies largely on the analysis of AES.La cryptographie moderne est fondée sur la notion de sécurité computationnelle. Les niveaux de sécurité attendus des cryptosystèmes sont exprimés en nombre d'opérations ; une attaque est un algorithme d'une complexité inférieure à la borne attendue. Mais ces niveaux de sécurité doivent aujourd'hui prendre en compte une nouvelle notion d'algorithme : le paradigme du calcul quantique. Dans le même temps,la délégation grandissante du chiffrement à des puces RFID, objets connectés ou matériels embarqués pose de nouvelles contraintes de coût.Dans cette thèse, nous étudions la sécurité des cryptosystèmes à clé secrète face à un adversaire quantique.Nous introduisons tout d'abord de nouveaux algorithmes quantiques pour les problèmes génériques de k-listes (k-XOR ou k-SUM), construits en composant des procédures de recherche exhaustive.Nous présentons ensuite des résultats de cryptanalyse dédiée, en commençant par un nouvel outil de cryptanalyse quantique, l'algorithme de Simon hors-ligne. Nous décrivons de nouvelles attaques contre les algorithmes Spook et Gimli et nous effectuons la première étude de sécurité quantique du chiffrement AES. Dans un troisième temps, nous spécifions Saturnin, une famille de cryptosystèmes à bas coût orientés vers la sécurité post-quantique. La structure de Saturnin est proche de celle de l'AES et sa sécurité en tire largement parti