Post-quantum key-encapsulation mechanisms (KEMs) usually use the well-known Fujisaki-Okamoto (FO) transformation during key decapsulation to achieve chosen ciphertext attack (CCA) security. In the FO transformation, the re-encryption procedure depends on the message. The side-channel leakage of re-encryption can be exploited to recover the coefficients of the secret key under chosen ciphertexts, even if the KEM scheme is CCA secure. However, it still requires a large amount of trace during the profiling and attacking phase. In this work, we introduce the first non-profiled deep learning-based attack on lattice-based KEMs. We propose a chosen ciphertexts method that is suitable for non-profiled deep learning-based attacks. The horizontal chunking strategy is employed to partition the coefficients into chunks, enabling the independent recovery of multiple secret key coefficients within each chunk. Then, we adopt an incremental learning strategy to allow the deep learning model to gradually learn the knowledge of each chunk. Moreover, we push the limits of traditional non-profiled deep learning-based attacks by combining the unsupervised domain adaptation with the correlation distinguisher, eliminating the necessity of neural network training for each possible key guess. The feasibility of the attack is verified by practical experiments for the unprotected and masked implementations of Kyber on the ARM Cortex-M4.

Masking is one of the most popular to guarantee the security of the cryptographic implementation against side-channel analysis. Low Entropy Masking Scheme (LEMS) has been proposed to relieve the high overhead by reducing the entropy of the mask. The masks are selected carefully to resist univariate first-order attacks. However, in practice, securely implementing masking can be a challenging task. In this work, we exhibit the vulnerability of LEMS implementation on FPGA. Firstly, we provide the security model to characterize the masking balance at the implementation level. Based on the security model, we exhibit a first-order leakage in the netlist of the implementation. It can be proved that this defect is due to the uncontrollable EDA tool to optimize the AES Sbox implementation on the specific architecture of FPGA during synthesis. The discovered flaw can be exhibited by performing a couple of first-order attacks to recover the secret key successfully on FPGA. The vulnerability is verified by simulation and practical measurements. Finally, the potential countermeasure and security evaluation process for LEMS are provided to avoid leakage during the pre-silicon design phase.

Profiled side-channel analysis presents a significant risk to embedded devices in Internet of Things (IoT). Typically, a single trace is insufficient to successfully key recovery in practical scenarios. It still requires several traces based on Bayes’ posterior probability. In this article, we introduce a chosen-plaintext (CP) strategy into the deep learning-based profiled attacks to improve the attack efficiency. First, we present a general strategy to profile the leakage model by exploiting the sensitivity analysis and clustering analysis. The leakage model derived from deep neural network is to characterize the leakage of the target algorithm. Second, we propose an adaptive CP method in the deep learning-based attack, transforming the conditional probability distribution of the leakage into the entropy of the key candidates under the profiled leakage model. Finally, we evaluate the efficiency of the attack by practical measurements. The results demonstrate that the proposed method requires fewer traces to retrieve the key of AES on devices of different types, e.g., Smartcard, FPGA, and ARM. Moreover, our attack improves the attack efficiency on masked implementations.

The purpose of this study is to present a novel method which can objectively identify the subjective perception of tonic pain. To achieve this goal, scalp EEG data are recorded from 16 subjects under the cold stimuli condition. The proposed method is capable of classifying four classes of tonic pai n states, which include No pain, Minor Pain, Moderate Pain, and Severe Pain. Due to multi-class problem of our research an extended Common Spatial Pattern (ECSP) method is first proposed for accurately extracting features of tonic pain from captured EEG data. Then, a single-hidden-layer feedforward network is used as a classifier for pain identification. With the aid of extreme learning machine (ELM) algorithm, the classifier is trained here. The advantages of ELM-based classifier can obtain an optimal and generalized solution for multi-class tonic cold pain. Experimental results demonstrate that the proposed method discriminates the tonic pain successfully. Additionally, to show the superiority for the ELM-based classifier, compared results with the well-known support vector machine (SVM) method show the ELM-based classifier outperform than the SVM-based classifier. These findings may pay the way for providing a direct and objective measure of the subjective perception of tonic pain.

Eye detection plays an important role in many fields, because eyes provide prominent facial feature information. However, changes in face pose, illumination variation, with glasses, and eye occlusions can make it difficult to detect eyes well from facial images. This paper proposes a hybrid model f or eye detection. The model is an integration of two classifiers: Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). In order to improve the speed of detection in the system, an eye variance filter (EVF) is constructed for eliminating most of noneye images to keep less candidate eye images. The CNN then works as a trainable feature extractor to explicitly extract various latent eye features. Finally, the trained SVM classifier is employed for eye verification instead of using the CNN classification function. Experiments applying the model have been conducted on the BioID, IMM, FERET and ORL face databases. Comparisons with other methods on the same databases indicate that this hybrid model has achieved a higher detection accuracy. Extensive experiments demonstrate the robustness and efficiency of our method by testing it on different facial images with varying eye conditions.