Background: The migration to post-quantum cryptography confronts resource-constrained Internet of Things (IoT) devices with a material performance cost. CRYSTALS-Dilithium, standardised as the Module-Lattice-Based Digital Signature Algorithm (ML-DSA) in FIPS 204, fixes the Keccak-based SHAKE functions as its only symmetric primitives, and profiling on embedded platforms identifies hashing as the largest single contributor to the scheme’s software cost. This review synthesises the performance...

Side-channel analysis (SCA) attacks rely on leakage from a target device. It is common to assume that linear operations implemented by XOR gates produce symmetric leakage and carry negligible side-channel information. In practice, leakage from XOR gates produces complex, non-independent, and time-varying asymmetric behavior. The paper introduces Feature Estimation based Attacks (FEbA) -- a dedicated profiling attack that exploits these asymmetries. The attack is versatile; it was...

Tosun and Savas (IEEE TIFS'23) proposed a non-profiling power analysis attack on masked ML-KEM, or CRYSTALS-Kyber. Their attack can recover a full secret key of Kyber with 7,000 power traces. Later, Tosun et al. (IEEE Access'24) claimed an improvement over the previous attack with only 550 traces, but the result is not convincing. In particular, their attack does not seem to recover a full secret key of masked Kyber; instead, it recovers only the absolute values for every coefficient of a...

This paper investigates a recent, claimed state-of-the-art attack on the ASCADv2 dataset, a higher-order masked and shuffled AES implementation, which we demonstrate to be a false positive. Despite successful validation using classical metrics, including a converging Guessing Entropy (GE), we prove that the model learned no actual side-channel leakage. Instead, it exploited a statistical bias in the intermediate value distribution. We argue that the usual scoring function used in the GE...

Deep Learning-based Non-profiled Side-Channel Analysis (DL-NSCA) enables automatic feature extraction without a profiling device, but existing approaches mainly target non-linear operations, requiring prior knowledge of the algorithm's unique non-linear structure and computable non-linear intermediate values. These limit applicability in analyzing proprietary or undisclosed implementations and in settings where plaintext/ciphertext are masked by unknown randomness (e.g., tweaks or...

Non profiled side-channel attacks aim at exploiting leakage traces from a targeted embedded system to extract secret information, without a priori knowledge on the true leakage model of the device. To automate and simplify attacks, deep learning techniques have thus been introduced in the side-channel community. Most of the works published have mainly explored the use of discriminative models in the profiled or non profiled context. However, the lack of interpretability and explainability of...

The rapid progress of Internet-of-Things (IoT) systems and network protocols has strengthened the demand for digital signature schemes with compact signatures and low computational overhead. However, standardized post-quantum signature schemes, such as ML-DSA, SLH-DSA, and Falcon, incur relatively large signature sizes, which limit their practicality on resource-constrained devices (RCD). To address this challenge, NIST recalled the post-quantum digital signature standardization process. It...

ML-DSA (formerly CRYSTALS-Dilithium), NIST’s primary post-quantum signature standard, is increasingly deployed along with the post-quantum transitions. Yet when the implementations of ML-DSA are deployed in practice, their physical security remains underexplored. In this work, we reveal a new attack surface against ML-DSA by exploiting the leakages from both rejected signing trials and the final accepted signing trial. We present, to the best of our knowledge, the first side-channel attack...

Non profiled attacks aim to recover secret information from a device without prior knowledge of its leakage model. However, most practical non profiled attacks, such as Differential Power Analysis, Correlation Power Analysis, and Linear Regression-based Attacks (LRA), still depend on a priori leakage assumptions. Designing a generic attack that does not rely on any such assumption therefore remains an open problem and has been actively investigated by the side-channel community for more than...

The transition to post-quantum cryptography (PQC) is accelerating due to the potential of quantum computing to compromise classical public-key cryptosystems. While standardized schemes such as CRYSTALS-Kyber, CRYSTALS-Dilithium, and SPHINCS+ offer strong theoretical security, practical deployments remain susceptible to physical-layer vulnerabilities, notably side-channel attacks (SCAs). SCAs exploit unintentional leakages in hardware and software implementations—such as power traces,...

With the advancement of deep learning techniques, Deep Learning-based Non-profiled Side-Channel Analysis (DL-NSCA) can automatically learn and combine features, making it a promising method that can skip the manual and precise selection of Points of Interest (PoIs). Existing DL-NSCA methods assume that the attacker can identify a short leakage interval (usually less than 5000 points) containing PoIs from raw traces (more than 100,000 points) and then feed the leakage interval into the...

In this work, we present methods for conducting higher-order non-profiled side-channel attacks on Lattice-Based Cryptography (LBC). Our analysis covers two scenarios: one where the device leakage is known and follows Hamming weight model, and another where the leakage model is not Hamming weight based and unknown to the attacker. We focus on the Post-Quantum Cryptography (PQC) standards, the Dilithium digital signature (i.e. ML-DSA) and the Kyber key encapsulation (i.e. ML-KEM) algorithms....

In the last years, Deep Learning algorithms have been browsed and applied to Side-Channel Analysis in order to enhance attack’s performances. In some cases, the proposals came without an indepth analysis allowing to understand the tool, its applicability scenarios, its limitations and the advantages it brings with respect to classical statistical tools. As an example, a study presented at CHES 2021 proposed a corrective iterative framework to perform an unsupervised attack which achieves a...

HuFu is an unstructured lattice-based signature scheme proposed during the NIST PQC standardization process. In this work, we present a side-channel analysis of HuFu's reference implementation. We first exploit the multiplications involving its two main secret matrices, recovering approximately half of their entries through a non-profiled power analysis with a few hundred traces. Using these coefficients, we reduce the dimension of the underlying LWE problem, enabling full secret key...

In this paper we provide new theoretical and empirical evidences that gradient-based deep learning profiling attacks (DL-SCA) suffer from masking schemes. This occurs through an initial stall of the learning process: the so-called plateau effect. To understand why, we derive an analytical expression of a DL-SCA model targeting simulated traces which enables us to study an analytical expression of the loss. By studying the loss landscape of this model, we show that not only do the magnitudes...

Side-channel attacks exploit information leaked through non-primary channels, such as power consumption, electromagnetic emissions, or timing, to extract sensitive data from cryptographic devices. Over the past three decades, side-channel analysis has evolved into a mature research field with well-established methodologies for analyzing standard cryptographic algorithms like the Advanced Encryption Standard (AES). However, the integration of side-channel analysis with formal methods remains...

In this study, we present the first side-channel attack on the ARADI block cipher, exposing its vulnerabilities to physical attacks in non-profiled scenarios. We propose a novel bitwise divide-and-conquer methodology tailored for ARADI, enabling key recovery. Furthermore, based on our attack approach, we present a stepwise method for recovering the full 256-bit master key. Through experiments on power consumption traces from an ARM processor, we demonstrate successful recovery of target key...

Modern micro-architectural attacks use a variety of building blocks chained to develop a final exploit. However, since in most cases, the footprint of such attacks is not visible architecturally (like, in the file-system), it becomes trickier to defend against these. In light of this, several automated defence mechanisms use Hardware Performance Counters (HPCs) detect when the micro-architectural elements are being misused for a potential attacks (like flush-reload, Spectre, Meltdown etc.)....

Resilience against side-channel attacks is an important consideration for cryptographic implementations deployed in devices with physical access to the device. However, noise in side-channel measurements has a significant impact on the complexity of these attacks, especially when an implementation is protected with masking. Therefore, it is important to assess the ability of an attacker to deal with noise. While some previous works have considered approaches to remove (some) noise from...

This paper questions the side-channel security of central reduction technique, which is widely adapted in efficient implementations of Lattice-Based Cryptography (LBC). We show that the central reduction leads to a vulnerability by creating a strong dependency between the power consumption and the sign of sensitive intermediate values. We exploit this dependency by introducing the novel absolute value prediction function, which can be employed in higher-order non-profiled multi-query...

We present a side-channel attack on CRYSTALS-Dilithium, a post-quantum secure digital signature scheme, with two variants of post-processing. The side-channel attack exploits information leakage in the secret key unpacking procedure of the signing algorithm to recover the coefficients of the polynomials in the secret key vectors ${\bf s}_1$ and ${\bf s}_2$ by profiled deep learning-assisted power analysis. In the first variant, one half of the coefficients of ${\bf s}_1$ and ${\bf s}_2$ is...

Deep learning-based profiling side-channel analysis has gained widespread adoption in academia and industry due to its ability to uncover secrets protected by countermeasures. However, to exploit this capability, an adversary must have access to a clone of the targeted device to obtain profiling measurements and know secret information to label these measurements. Non-profiling attacks avoid these constraints by not relying on secret information for labeled data. Instead, they attempt all...

This paper formally analyzes two major non-profiled deep-learning-based side-channel attacks (DL-SCAs): differential deep-learning analysis (DDLA) by Timon and collision DL-SCA by Staib and Moradi. These DL-SCAs leverage supervised learning in non-profiled scenarios. Although some intuitive descriptions of these DL-SCAs exist, their formal analyses have been rarely conducted yet, which makes it unclear why and when the attacks succeed and how the attack can be improved. In this paper, we...

Homomorphic encryption (HE) allows computing encrypted data in the ciphertext domain without knowing the encryption key. It is possible, however, to break fully homomorphic encryption (FHE) algorithms by using side channels. This article demonstrates side-channel leakages of the Microsoft SEAL HE library. The proposed attack can steal encryption keys during the key generation phase by abusing the leakage of ternary value assignments that occurs during the number theoretic transform (NTT)...

Side-channel Collision Attacks (SCCA) constitute a subset of non-profiling attacks that exploit information dependency leaked during cryptographic operations. Unlike traditional collision attacks, which seek instances where two different inputs to a cryptographic algorithm yield identical outputs, SCCAs specifically target the internal state, where identical outputs are more likely. In CHES 2023, Staib et al. presented a Deep Learning-based SCCA (DL-SCCA), which enhanced the attack...

Lattice-based cryptographic schemes such as Crystals-Kyber and Dilithium are post-quantum algorithms selected to be standardized by NIST as they are considered to be secure against quantum computing attacks. The multiplication in polynomial rings is the most time-consuming operation in many lattice-based cryptographic schemes, which is also subject to side-channel attacks. While NTT-based polynomial multiplication is almost a norm in a wide range of implementations, a relatively new method,...

The threat of physical side-channel attacks and their countermeasures is a widely researched field. Most physical side-channel attacks rely on the unavoidable influence of computation or storage on voltage or current fluctuations. Such data-dependent influence can be exploited by, for instance, power or electromagnetic analysis. In this work, we introduce a novel non-invasive physical side-channel attack, which exploits the data-dependent changes in the impedance of the chip. Our attack...

Deep learning-based profiling side-channel analysis is widely adopted in academia and industry thanks to the ability to reveal secrets protected with countermeasures. To leverage its capability, the adversary needs to have access to a clone of an attack device to obtain the profiling measurements. Moreover, the adversary needs to know secret information to label these measurements. Non-profiling attacks avoid those constraints by not relying on secret information to label data but rather by...

With the advent of secure function evaluation (SFE), distrustful parties can jointly compute on their private inputs without disclosing anything besides the results. Yao’s garbled circuit protocol has become an integral part of secure computation thanks to considerable efforts made to make it feasible, practical, and more efficient. These efforts have resulted in multiple optimizations on this primitive to enhance its performance by orders of magnitude over the last years. The advancement in...

A Feistel Network (FN) based block cipher relies on a Substitution Box (S-Box) for achieving the non-linearity. S-Box is carefully designed to achieve optimal cryptographic security bounds. The research of the last three decades shows that considerable efforts are being made on the mathematical design of an S-Box. To import the exact cryptographic profile of an S-Box, the designer focuses on the Affine Equivalent (AE) or Extended Affine (EA) equivalent S-Box. In this research, we argue that...

Post-quantum cryptography represents a category of cryptosystems resistant to quantum algorithms. Recently, NIST launched a process to standardize one or more of such algorithms in the key encapsulation mechanism and signature categories. Such schemes are under the scrutiny of their mathematical security, but they are not side-channel secure at the algorithm level. That is why their side-channel vulnerabilities must be assessed by the research community. In this paper, we present a...

Side-channel attacks are powerful non-invasive attacks on cryptographic algorithms. Among such attacks, profiling attacks have a prominent place as they assume an attacker with access to a copy of the device under attack. The attacker uses the device's copy to learn as much as possible about the device and then mount the attack on the target device. In the last few years, Machine Learning has been successfully used in profiling attacks, as such techniques proved to be capable of breaking...

This study derives information-theoretical bounds of the success rate (SR) of side-channel attacks on masked implementations. We first develop a communication channel model representing side-channel attacks on masked implementations. We then derive two SR bounds based on the conditional probability distribution and mutual information of shares. The basic idea is to evaluate the upper-bound of the mutual information between the non-masked secret value and the side-channel trace by the...

Recent works showed how Mutual Information Neural Estimation (MINE) could be applied to side-channel analysis in order to evaluate the amount of leakage of an electronic device. One of the main advantages of MINE over classical estimation techniques is to enable the computation between high dimensional traces and a secret, which is relevant for leakage assessment. However, optimally exploiting this information in an attack context in order to retrieve a secret remains a non-trivial task...

Deep Learning Side-Channel Attacks (DLSCAs) have become a realistic threat to implementations of cryptographic algorithms, such as Advanced Encryption Standard (AES). By utilizing deep-learning models to analyze side-channel measurements, the attacker is able to derive the secret key of the cryptographic alrgorithm. However, when traces have multiple leakage intervals for a specific attack point, the majority of existing works train neural networks on these traces directly, without a...

Automated methods have become crucial components when searching for distinguishers against symmetric-key cryptographic primitives. While MILP and SAT solvers are among the most popular tools to model ciphers and perform cryptanalysis, other methods with different performance profiles are appearing. In this article, we explore the use of Constraint Programming (CP) for differential cryptanalysis on the ASCON authenticated encryption family (first choice of the CAESAR lightweight applications...

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 side-channel leakages, and most of the state-of-the-art attacks have been shown to work on systems implementing AES. The methodology is usually based on exploiting leakages for the outer rounds, i.e., the first and the last...

Physical Unclonable Functions (PUFs) have been increasingly used as an alternative to non-volatile memory for the storage of cryptographic secrets. Research on side channel and fault attacks with the goal of extracting these secrets has begun to gain interest but no fault injection attack targeting the necessary error correction within a PUF device has been shown so far. This work demonstrates one such attack on a hardware fuzzy commitment scheme implementation and thus shows a new potential...

Practical side-channel attacks on recent devices may be challenging due to the poor quality of acquired signals. It can originate from different factors, such as the growing architecture complexity, especially in System-on-Chips, creating unpredictable and concurrent operation of multiple signal sources on the device. This work makes use of mixture distributions to formalize this complexity, allowing us to explain the benefit of using a technique like Scatter, where different samples of the...

Recently, research on deep learning based side-channel analysis (DLSCA) has received a lot of attention. Deep learning-based profiling methods similar to template attacks as well as non-profiling-based methods similar to differential power analysis have been proposed. DLSCA methods have been proposed for targets to which masking schemes or jitter-based hiding schemes are applied. However, most of them are methods for finding the secret key, except for methods for preprocessing, and there are...

Nowadays, the resistance against algebraic attacks and fast algebraic attacks are considered as an important cryptographic property for Boolean functions used in stream ciphers. Both attacks are very powerful analysis concepts and can be applied to symmetric cryptographic algorithms used in stream ciphers. The notion of algebraic immunity has received wide attention since it is a powerful tool to measure the resistance of a Boolean function to standard algebraic attacks. Nevertheless, an...

At CRYPTO 2019, Gohr first introduces the deep learning based cryptanalysis on round-reduced SPECK. Using a deep residual network, Gohr trains several neural network based distinguishers on 8-round SPECK-32/64. The analysis follows an `all-in-one' differential cryptanalysis approach, which considers all the output differences effect under the same input difference. Usually, the all-in-one differential cryptanalysis is more effective compared to the one using only one single differential...

In recent years, deep learning-based side-channel attacks have established their position as mainstream. However, most deep learning techniques for cryptanalysis mainly focused on classifying side-channel information in a profiled scenario where attackers can obtain a label of training data. In this paper, we introduce a novel approach with deep learning for improving side-channel attacks, especially in a non-profiling scenario. We also propose a new principle of training that trains an...

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...

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 the same time, be insensible to noisy (or non-leaking) samples. This outlines a generalization situation where the model can identify the main...

In this work, we demonstrate generic and practical side-channel assisted chosen ciphertext attacks on multiple LWE/LWR-based Public Key Encryption (PKE) and Key Encapsulation Mechanism (KEM) secure in the chosen ciphertext model (IND-CCA security). Firstly, we identified EM-based side-channel vulnerabilities in the error correcting codes (ECC) used in LWE/LWR-based schemes that enable to distinguish the value/validity of the codewords output from the decryption operation. We also identified...

Leakages during the signing process, including partial key exposure and partial (or complete) randomness exposure, may be devastating for the security of digital signatures. In this work, we investigate the security of lattice-based Fiat-Shamir signatures in the presence of randomness leakage. To this end, we present a generic key recovery attack that relies on minimum leakage of randomness, and then theoretically connect it to a variant of Integer-LWE (ILWE) problem. The ILWE problem,...

In recent years, deep learning has become an attractive ingredient to side-channel analysis (SCA) due to its potential to improve the success probability or enhance the performance of certain frequently executed tasks. One task that is commonly assisted by machine learning techniques is the profiling of a device's leakage behavior in order to carry out a template attack. At CHES 2019, deep learning has also been applied to non-profiled scenarios for the first time, extending its reach within...

Profiling side-channel attacks are considered the most potent form of side-channel attacks. They consist of two steps. First, the adversary builds a leakage model using a device similar to the target one. This leakage model is then exploited to extract the secret information from the victim's device. These attacks can be seen as a classification problem, where the adversary needs to decide to what class (and consequently, the secret key) the traces collected from the victim's device belong....

Parallel cryptographic implementations are generally considered to be more advantageous than their non-parallel counterparts in mitigating side-channel attacks because of their higher noise-level. So far as we know, the side-channel security of GPU-based cryptographic implementations have been studied in recent years, and those implementations then turn out to be susceptible to some side-channel attacks. Unfortunately, the target parallel implementations in their work do not achieve strict...

As one of the most prevalent SCA countermeasures, masking schemes are designed to defeat a broad range of side channel attacks. An attack vector that is suitable for low-order masking schemes is to try and directly determine the mask(s) (for each trace) by utilising the fact that often an attacker has access to several leakage points of the respectively used mask(s). Good examples for implementations of low order masking schemes are the based on table re-computations and also the masking...

Deep Learning has recently been introduced as a new alternative to perform Side-Channel analysis. Until now, studies have been focused on applying Deep Learning techniques to perform Profiled Side-Channel attacks where an attacker has a full control of a profiling device and is able to collect a large amount of traces for different key values in order to characterize the device leakage prior to the attack. In this paper we introduce a new method to apply Deep Learning techniques in a...

Since the seminal work of Boneh et al., the threat of fault attacks has been widely known and techniques for fault attacks and countermeasures have been studied extensively. The vast majority of the literature on fault attacks focuses on the ability of fault attacks to change an intermediate value to a faulty one, such as differential fault analysis (DFA), collision fault analysis, statistical fault attack (SFA), fault sensitivity analysis, or differential fault intensity analysis (DFIA)....

Side-channel attacks are a threat to cryptographic algorithms running on embedded devices. Public-key cryptosystems, including elliptic curve cryptography (ECC), are particularly vulnerable because their private keys are usually long-term. Well known countermeasures like regularity, projective coordinates and scalar randomization, among others, are used to harden implementations against common side-channel attacks like DPA. Horizontal clustering attacks can theoretically overcome these...

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...