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 autoencoder through the noise from real data using noise-reduced labels. It notably diminishes the noise in measurements by modifying the autoencoder framework to the signal preprocessing. We present convincing comparisons on our custom dataset, captured from ChipWhisperer-Lite board, that demonstrate our approach outperforms conventional preprocessing methods such as principal component analysis and linear discriminant analysis. Furthermore, we apply the proposed methodology to realign de-synchronized traces that applied hiding countermeasures, and we experimentally validate the performance of the proposal. Finally, we experimentally show that we can improve the performance of higher-order side-channel attacks by using the proposed technique with domain knowledge for masking countermeasures.

The differential deep learning analysis proposed by Timon is the first non-profiled side-channel attack technique that uses deep learning. The technique recovers the secret key using the phenomenon of deep learning metrics. However, the proposed technique made it difficult to observe the results from the intermediate process, while the neural network had to be retrained repeatedly, as the cost of learning increased with key size. In this paper, we propose three methods to solve the aforementioned problems and any challenges resulting from solving these problems. First, we propose a modified algorithm that allows the monitoring of the metrics in the intermediate process. Second, we propose a parallel neural network architecture and algorithm that works by training a single network, with no need to re-train the same model repeatedly. Attacks were performed faster with the proposed algorithm than with the previous algorithm. Finally, we propose a novel architecture that uses shared layers to solve memory problems in parallel architecture and also helps achieve better performance. We validated the performance of our methods by presenting the results of non-profiled attacks on the benchmark database ASCAD and for a custom dataset on power consumption collected from ChipWhisperer-Lite. On the ASCAD database, our shared layers method was up to 134 times more efficient than the previous method.

As side‐channel analysis and machine learning algorithms share the same objective of classifying data, numerous studies have been proposed for adapting machine learning to side‐channel analysis. Howe...

The Gaussian sampler is an integral part in lattice-based cryptography as it has a direct connection to security and efficiency. Although it is theoretically secure to use the Gaussian sampler, the security of its implementation is an open issue. Therefore, researchers have started to investigate the security of the Gaussian sampler against side-channel attacks. Since the performance of the Gaussian sampler directly affects the performance of the overall cryptosystem, countermeasures considering only timing attacks are applied in the literature. In this paper, we propose the first single trace power analysis attack on a constant-time cumulative distribution table (CDT) sampler used in lattice-based cryptosystems. From our analysis, we were able to recover every sampled value in the key generation stage, so that the secret key is recovered by the Gaussian elimination. By applying our attack to the candidates submitted to the National Institute of Standards and Technology (NIST), we were able to recover over 99% of the secret keys. Additionally, we propose a countermeasure based on a look-up table. To validate the efficiency of our countermeasure, we implemented it in Lizard and measure its performance. We demonstrated that the proposed countermeasure does not degrade the performance.

Bit permutations are efficient linear functions often used for lightweight cipher designs. However, they have low diffusion effects, compared to word-oriented binary and maximum distance separable (MDS) matrices. Thus, the security of bit permutation-based ciphers is significantly affected by differential and linear branch numbers (DBN and LBN) of nonlinear functions. In this paper, we introduce a widely applicable method for constructing S-boxes with high DBN and LBN. Our method exploits constructions of S-boxes from smaller S-boxes and it derives/proves the required conditions for smaller S-boxes so that the DBN and LBN of the constructed S-boxes are at least 3. These conditions enable us to significantly reduce the search space required to create such S-boxes. Using the unbalanced- Bridge and unbalanced- MISTY structures, we develop a variety of new lightweight S-boxes that provide not only both DBN and LBN of at least 3 but also efficient bitsliced implementations including at most 11 nonlinear bitwise operations. The new S-boxes are the first that exhibit these characteristics.

Over the past decade, decentralized cryptocurrencies have received attention in industry and academia. Hardware wallets are dedicated devices that manage cryptocurrencies safely without entrusting cryptographic keys to a third party. Side-channel attacks have been widely studied in cryptanalysis and have already been proven threatening, but analysis on hardware wallets still needs to be researched. Although the previous work demonstrated several side-channel vulnerabilities, their attacks require a finely controlled environment or a learning phase of target devices’ physical properties before the attacks. This paper proposes a side-channel attack on hardware wallets extracting private keys. The proposed attack needs a single power trace measured when wallets process elliptic curve scalar multiplication with private keys. Our attack is reasonable since we do not damage the device under attack and do not target a specific device but an algorithm; it is widely applicable to wallets using that algorithm or analogous ones. It also presents the attack results conducted with three datasets: simulation, ChipWhisperer, and actual dataset collected from the Trezor Model One, the first and representative hardware wallets which comply with the de facto standard of hardware wallets.