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Bilevel optimization problems are characterized by an interactive hierarchical structure, where the upper level seeks to optimize its strategy while simultaneously considering the response of the lower level. Evolutionary algorithms are commonly used to solve complex bilevel problems in practical scenarios, but they face significant resource consumption challenges due to the nested structure imposed by the implicit lower-level optimality condition. This challenge becomes even more pronounced as problem dimensions increase. Although recent methods have enhanced bilevel convergence through task-level knowledge sharing, further efficiency improvements are still hindered by redundant lower-level iterations that consume excessive resources while generating unpromising solutions. To overcome this challenge, this paper proposes an efficient dynamic resource allocation framework for evolutionary bilevel optimization, named DRC-BLEA. Compared to existing approaches, DRC-BLEA introduces a novel competitive quasi-parallel paradigm, in which multiple lower-level optimization tasks, derived from different upper-level individuals, compete for resources. A continuously updated selection probability is used to prioritize execution opportunities to promising tasks. Additionally, a cooperation mechanism is integrated within the competitive framework to further enhance efficiency and prevent premature convergence. Experimental results compared with chosen state-of-the-art algorithms demonstrate the effectiveness of the proposed method. Specifically, DRC-BLEA achieves competitive accuracy across diverse problem sets and real-world scenarios, while significantly reducing the number of function evaluations and overall running time., Comment: This work has been submitted to the IEEE for possible publication

In recent years, despite significant advancements in adversarial attack research, the security challenges in cross-modal scenarios, such as the transferability of adversarial attacks between infrared, thermal, and RGB images, have been overlooked. These heterogeneous image modalities collected by different hardware devices are widely prevalent in practical applications, and the substantial differences between modalities pose significant challenges to attack transferability. In this work, we explore a novel cross-modal adversarial attack strategy, termed multiform attack. We propose a dual-layer optimization framework based on gradient-evolution, facilitating efficient perturbation transfer between modalities. In the first layer of optimization, the framework utilizes image gradients to learn universal perturbations within each modality and employs evolutionary algorithms to search for shared perturbations with transferability across different modalities through secondary optimization. Through extensive testing on multiple heterogeneous datasets, we demonstrate the superiority and robustness of Multiform Attack compared to existing techniques. This work not only enhances the transferability of cross-modal adversarial attacks but also provides a new perspective for understanding security vulnerabilities in cross-modal systems.

After three rounds of post-quantum cryptography (PQC) strict evaluations conducted by the national institute of standards and technology (NIST), CRYSTALS-Kyber was successfully selected in July 2022 and standardized in August 2024. It becomes urgent to further evaluate Kyber's physical security for the upcoming deployment phase. In this brief, we present an improved two-step attack on Kyber to quickly recover the full secret key, s, by using much fewer energy traces and less time. In the first step, we use the correlation power analysis (CPA) to obtain a portion of guess values of s with a small number of energy traces. The CPA is enhanced by utilizing both the Pearson and Kendall's rank correlation coefficients and modifying the leakage model to improve the accuracy. In the second step, we adopt the lattice attack to recover s based on the results of CPA. The success rate is largely built up by constructing a trial-and-error method. We implement the proposed attack for the reference implementation of Kyber512 (4 128-value groups of s) on ARM Cortex-M4 and successfully recover a 128-value group of s in about 9 minutes using a 16-core machine. Additionally, in that case, we only cost at most 60 CPA guess values for a group and 15 power traces for a guess., Comment: Submitted to ICCAD in May 2024

CRYSTALS-Kyber (a.k.a. Kyber) has been drafted to be standardized as the only key encapsulation mechanism (KEM) scheme by the national institute of standards and technology (NIST) to withstand attacks by large-scale quantum computers. However, the side-channel attack (SCA) on its implementation is still needed to be well considered for the upcoming migration. In this brief, we propose a secure and efficient hardware implementation for Kyber by incorporating a novel compact shuffling architecture. First of all, we modify the Fisher-Yates shuffle to make it more hardware-friendly. We then design an optimized shuffling architecture for the well-known open-source Kyber hardware implementation to enhance the security of all the potential side-channel leakage points. Finally, we implement the modified Kyber design on FPGA and evaluate its security and performance. The security is verified by conducting the correlation power analysis (CPA) attacks on the hardware. Meanwhile, FPGA place-and-route results show that the proposed design reports only 8.7% degradation on the hardware efficiency compared with the original unprotected version, much better than existing hiding schemes.

Real-world multiobjective optimization problems usually involve conflicting objectives that change over time, which requires the optimization algorithms to quickly track the Pareto optimal front (POF) when the environment changes. In recent years, evolutionary algorithms based on prediction models have been considered promising. However, most existing approaches only make predictions based on the linear correlation between a finite number of optimal solutions in two or three previous environments. These incomplete information extraction strategies may lead to low prediction accuracy in some instances. In this paper, a novel prediction algorithm based on incremental support vector machine (ISVM) is proposed, called ISVM-DMOEA. We treat the solving of dynamic multiobjective optimization problems (DMOPs) as an online learning process, using the continuously obtained optimal solution to update an incremental support vector machine without discarding the solution information at earlier time. ISVM is then used to filter random solutions and generate an initial population for the next moment. To overcome the obstacle of insufficient training samples, a synthetic minority oversampling strategy is implemented before the training of ISVM. The advantage of this approach is that the nonlinear correlation between solutions can be explored online by ISVM, and the information contained in all historical optimal solutions can be exploited to a greater extent. The experimental results and comparison with chosen state-of-the-art algorithms demonstrate that the proposed algorithm can effectively tackle dynamic multiobjective optimization problems., Comment: 13 pages,4 figures, and 4 tables