Optimal Collision Side-Channel Attacks

Collision side-channel attacks are efficient attacks against cryptographic implementations, however, optimal collision side-channel attacks and how to compute them efficiently is an open question. In this paper, we show that collision side-channel attacks can be derived using the maximum likelihood principle when the distribution of the values of the leakage function is known. This allows us to exhibit the optimal collision side-channel attack and its efficient computation. Finally, we are able to compute an upper bound for the success rate of the optimal post-processing strategy, and we show that our method and the optimal strategy have success rates close to each other. Attackers can benefit from our method as we present an efficient collision side-channel attack. Evaluators can benefit from our method as we present a tight upper bound for the success rate of the optimal strategy

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

How to Launch a Powerful Side-Channel Collision Attack?

Benefiting from its independence of leakage model, side-channel collision attack is one of the most common distinguishers and attracts wide attention. Although several improvements have been given, its performance on attacking a single collision value has not been significantly improved. Its optimization and efficiency is still an open problem. To solve this, we theoretically analyze the quantitative relationship between encryptions and collisions in this paper, and propose an efficient side-channel attack named Collision-Paired Correlation Attack (CPCA) for low noise scenarios to guarantee that the side with fewer samples in a collision to be detected is completely paired. This optimizes the inefficient utilization of collision information in the existing collision attacks. Moreover, to further exploit the collision information, we maximize the collision pairing, and this optimization significantly improves CPCA and extends our CPCA to large noise scenarios. Finally, to reduce computation complexity, we further optimize our CPCA to a CPA-like distinguisher. Our further theoretical study fully illustrates that our CPCA provides the upper security bound of CECA, and experimental results fully show its superiority

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

Multiple-Differential Mechanism for Collision-Optimized Divide-and-Conquer Attacks

Several combined attacks have shown promising results in recovering cryptographic keys by introducing collision information into divide-and-conquer attacks to transform a part of the best key candidates within given thresholds into a much smaller collision space. However, these Collision-Optimized Divide-and-Conquer Attacks (CODCAs) uniformly demarcate the thresholds for all sub-keys, which is unreasonable. Moreover, the inadequate exploitation of collision information and backward fault tolerance mechanisms of CODCAs also lead to low attack efficiency. Finally, existing CODCAs mainly focus on improving collision detection algorithms but lack theoretical basis. We exploit Correlation-Enhanced Collision Attack (CECA) to optimize Template Attack (TA). To overcome the above-mentioned problems, we first introduce guessing theory into TA to enable the quick estimation of success probability and the corresponding complexity of key recovery. Next, a novel Multiple-Differential mechanism for CODCAs (MD-CODCA) is proposed. The first two differential mechanisms construct collision chains satisfying the given number of collisions from several sub-keys with the fewest candidates under a fixed probability provided by guessing theory, then exploit them to vote for the remaining sub-keys. This guarantees that the number of remaining chains is minimal, and makes MD-CODCA suitable for very high thresholds. Our third differential mechanism simply divides the key into several large non-overlapping ``blocks\u27\u27 to further exploit intra-block collisions from the remaining candidates and properly ignore the inter-block collisions, thus facilitating the latter key enumeration. The experimental results show that MD-CODCA significantly reduces the candidate space and lowers the complexity of collision detection, without considerably reducing the success probability of attacks

Comprehensive Designs of Innovate Secure Hardware Devices against Machine Learning Attacks and Power Analysis Attacks

Hardware security is an innovate subject oriented from growing demands of cybersecurity and new information vulnerabilities from physical leakages on hardware devices. However, the mainstream of hardware manufacturing industry is still taking benefits of products and the performance of chips as priority, restricting the design of hardware secure countermeasures under a compromise to a finite expense of overheads. Consider the development trend of hardware industries and state-of-the-art researches of architecture designs, this dissertation proposes some new physical unclonable function (PUF) designs as countermeasures to side-channel attacks (SCA) and machine learning (ML) attacks simultaneously. Except for the joint consideration of hardware and software vulnerabilities, those designs also take efficiencies and overhead problems into consideration, making the new-style of PUF more possible to be merged into current chips as well as their design concepts. While the growth of artificial intelligence and machine-learning techniques dominate the researching trends of Internet of things (IoT) industry, some mainstream architectures of neural networks are implemented as hypothetical attacking model, whose results are used as references for further lifting the performance, the security level, and the efficiency in lateral studies. In addition, a study of implementation of neural networks on hardware designs is proposed, this realized the initial attempt to introduce AI techniques to the designs of voltage regulation (VR). All aforementioned works are demonstrated to be of robustness to threats with corresponding power attack tests or ML attack tests. Some conceptional models are proposed in the last of the dissertation as future plans so as to realize secure on-chip ML models and hardware countermeasures to hybrid threats

Formal Analysis of Non-profiled Deep-learning Based Side-channel Attacks

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 provide the first information-theoretical analysis of DDLA. We reveal its relevance to the mutual information analysis (MIA), and then present three theorems stating some limitations and impossibility results of DDLA. Subsequently, we provide the first probability-theoretical analysis on collision DL-SCA. After presenting its formalization with a proposal of our distinguisher for collision DL-SCA, we prove its optimality. Namely, we prove that the collision DL-SCA using our distinguisher theoretically maximizes the success rate if the neural network (NN) training is completely successful (namely, the NN completely imitates the true conditional probability distribution). Accordingly, we propose an improvement of the collision DL-SCA based on a dedicated NN architecture and a full-key recovery methodology using multiple neural distinguishers. Finally, we experimentally evaluate non-profiled (DL-)SCAs using a newly created dataset using publicly available first-order masked AES implementation. The existing public dataset of side-channel traces is insufficient to evaluate collision DL-SCAs due to a lack of substantive side-channel traces for different key values. Our dataset enables a comprehensive evaluation of collision (DL-)SCAs, which clarifies the current situation of non-profiled (DL-)SCAs

Optimal Collision Side-Channel Attacks

International audienceCollision side-channel attacks are effective attacks against cryptographic implementations, however, optimality and efficiency of collision side-channel attacks is an open question. In this paper, we show that collision side-channel attacks can be derived using maximum likelihood principle when the distribution of the values of the leakage function is known. This allows us to exhibit the optimal collision side-channel attack and its efficient computation. Finally, we can compute an upper bound for the success rate of the optimal post-processing strategy, and we show that our method and the optimal strategy have success rates close to each other. Attackers can benefit from our method as we present an efficient collision side-channel attack. Evaluators can benefit from our method as we present a tight upper bound for the success rate of the optimal strategy