By introducing collision information, the existing side-channel Correlation-Enhanced Collision Attacks (CECAs) performed collision-chain detection, quickly filtered out candidates unsatisfying collision conditions and extracted a part of optimal candidates for further process, thereby rapidly and significantly reducing the key candidate space and the difficulty of key recovery. However, they are still limited by disadvantages such as serial implementation, complex parameter settings and lack of intelligence, resulting in a low success rate of key recovery. To address these issues, we first present a Collision Detection framework with Genetic Algorithm (CDGA), which exploits Genetic Algorithm to detect the collision chains and has a strong capability of global searching. Secondly, we theoretically analyze the performance of CECA, and bound the searching depth of its output candidate vectors with a confidence level using a data-driven hypothesis test that provides confidence bounds for Gaussian leakages and an approximation based on Central Limit Theory (CLT)for non-Gaussian cases, which facilitates effective and stable population initialization. Thirdly, benefiting from our hypothesis-test-guided design, we propose a goal-directed mutation that prioritizes promising collision candidates, thus improving efficiency and adaptability of the CDGA. Finally, to optimize the evolution of CDGA, we introduce a roulette selection strategy to employ a probability assignment based on individual fitness values to guarantee the preferential selection of superior genes. Comprehensive experiments on DPA Contest v4.1 (AES-256 with Rotated S-boxes Masking) and an AT89S52 AES-128 platform demonstrate that CDGA achieves faster convergence and higher key-recovery success rates compared with TOC/FTC/FCC and Wiemers’ cumulative-correlation selection.

The proliferation of the Internet of Things (IoT) has amplified the necessity for secure data transmission. Lightweight block ciphers are pivotal in fortifying the security of IoT systems, yet the resource-constrained nature of IoT often limits the complexity of their designs, especially the linear layer in typical substitution-permutation network (SPN) and Feistel designs. This study investigates the vulnerability of seven lightweight block ciphers—specifically, CRAFT, Midori, SKINNY, MANTIS, LBlock, TWINE, and WARP, each catering to diverse application demands—concerning their susceptibility to mixture differential (MD) cryptanalysis. Modifying an automated tool based on linear programming, we identify MD distinguishers associated with these ciphers, exhibiting a higher number of rounds than that observed in the widely adopted AES block cipher. This disparity suggests that the simplified linear layer adopted by the lightweight block ciphers potentially compromises their resistance to MD distinguisher construction. Nevertheless, this compromise is counterbalanced by an augmented incorporation of rounds within the cipher design. Pertaining to the notion of security margins—denoting the percentage of rounds beyond the scope of constructed MD distinguishers relative to the total number of full rounds—the lightweight block ciphers subjected to scrutiny manifest elevated security margins, thereby demonstrating heightened resilience against MD cryptanalysis. This research provides a comprehensive security evaluation of the target lightweight block ciphers and introduces a versatile evaluation tool that can be adapted for the analysis of other aligned lightweight block ciphers in the context of MD cryptanalysis.

With the proliferation of IoT devices today, the need to strengthen the security of these devices is becoming increasingly urgent, particularly the need to review the security of lightweight block ciphers. SIMON is a lightweight block cipher proposed by the National Security Agency (NSA) of US to provide efficient and secure encryption for resource-constrained devices in IoT systems. This paper aims to evaluate the security of SIMON against mixture differential cryptanalysis, which was proposed in Eurocrypt 2017 to launch the best key-recovery attacks on the most widely used encryption standard AES. Though there have been intensive studies on this cryptanalysis method, its current targets are all aligned block ciphers. Whether the numerous bitwise block ciphers, including SIMON, have weaknesses regarding this method remains unknown. In this paper, we extend the mixture differential cryptanalysis to bit-wise ciphers and develop an SAT-based automatic tool to search for such distinguishers. We interpret the bit-wise mixture differential distinguisher as a variant of differential distinguisher in the multi-key setting with 2-3n as the boundary (n:block size), potentially boosting rounds or improving the signal-to-noise ratio of previous boomerang or classical differential distinguisher. Using SIMON as an example, we discover multi-key distinguishers for up to 17-round SIMON32, 18-round SIMON48, and 23-round SIMON64, which outperform previous results in terms of the number of rounds. This paper reconciles the disparity between mixture differential cryptanalysis applied to word-oriented target ciphers and its application to bit-oriented targets, thereby extending the mixture differential cryptanalysis to a broader range of block ciphers.

By introducing collision information into divide-and-conquer distinguishers, the existing collision-optimized side-channel attacks transform the given candidate space into a significantly smaller collision space, thus achieving more efficient key recovery. However, the candidates of the first several subkeys shared by collision chains are still repeatedly detected, which happens very frequently and brings huge computational overhead. To alleviate this, we propose a highly efficient collision-optimized attack named collision tree (CoTree). This collision detection tool exploits tree structure to store the chains created from the same subchain on the same branch, thus significantly reducing the storage requirements. It then benefits from the properties of both tree and collisions and exploits a top-down tree building procedure and traverses each node only once when detecting their collisions with a candidate of the subkey currently under consideration. Finally, unlike the traditional top-down node removal, CoTree launches a bottom-up branch removal procedure to remove the chains unsatisfying the collision conditions from the tree after traversing all the considered candidates of this subkey, thus avoiding the traversal of the branches satisfying the collision condition. These strategies make our CoTree significantly alleviate the repetitive collision detection, and our experiments verify that it significantly outperforms the existing works.

Existing power trace extractors consider the case where the number of power traces available to the attacker is sufficient to guarantee successful attacks, and the goal of power trace extraction is to extract a small part of traces with high signal-to-noise ratio (SNR) to reduce the complexity of attacks rather than to increase the success rates. Although strict theoretical proofs are given, the existing power trace extractors are too simple and leakage characteristics of Points-of-Interest (POIs) have not been thoroughly analyzed. They only maximize the variance of the data-dependent power consumption component and ignore the noise component, which results in very limited SNR that hampers the performance of extractors. In this article, we provide a rigorous theoretical analysis of SNR of power traces, and propose a simple yet efficient SNR-centric extractor, named shortest distance first (SDF), to extract power traces with the smallest estimated noise by taking advantage of known plaintexts. In addition, to maximize the variance of the exploitable component while minimizing the noise, we refer to the SNR estimation model and propose another novel extractor named maximizing estimated SNR first (MESF). Finally, we further propose an advanced extractor called mean-optimized MESF (MMESF) that exploits the mean power consumption of each plaintext byte value to more accurately and reasonably estimate the data-dependent power consumption of the corresponding samples. Experiments on both simulated power traces and measurements from an ATmega328p micro-controller demonstrate the superiority of our new extractors.

Profiling attack based on smaller and deeper neural network VGGNet is performed on a smart-card CRT-RSA implementation. CRT-RSA implementation uses security countermeasures including masking and time jittering. An ad-hoc method is applied to extract points from traces to perform an effective deep learning profiling attack. State-of-the-art convolutional networks are trained and compared on our data set. Experiment results show that the VGG13 network achieves the best performance with less training time, which can be a start point balancing performance and computational resources.