Weighted Random k Satisfiability for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e4005" altimg="si2.svg"><mml:mrow><mml:mi>k</mml:mi><mml:mo linebreak="goodbreak" linebreakstyle="after">=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:math> (r2SAT) in Discrete Hopfield Neural Network

Zamri, Nur Ezlin; Azhar, Siti Aishah; Mansor, Mohd. Asyraf; Alway, Alyaa; Kasihmuddin, Mohd Shareduwan Mohd

doi:10.1016/j.asoc.2022.109312

Cited by 75 publications

(13 citation statements)

References 33 publications

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“…Therefore, the key degradation issue can be addressed by SFGA-CPA, ensuring that the recovered correct bytes do not turn into erroneous ones. Meanwhile, these three methods underwent Friedman testing when the number of occurrences of key degradation in the population was used as the metric [28,29]. The test results are presented in Table 2.…”

Section: Real Experiments Of Sakura-gmentioning

confidence: 99%

SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

Liu,

Li,

et al. 2024

CMC

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Correlation power analysis (CPA) combined with genetic algorithms (GA) now achieves greater attack efficiency and can recover all subkeys simultaneously. However, two issues in GA-based CPA still need to be addressed: key degeneration and slow evolution within populations. These challenges significantly hinder key recovery efforts. This paper proposes a screening correlation power analysis framework combined with a genetic algorithm, named SFGA-CPA, to address these issues. SFGA-CPA introduces three operations designed to exploit CPA characteristics: propagative operation, constrained crossover, and constrained mutation. Firstly, the propagative operation accelerates population evolution by maximizing the number of correct bytes in each individual. Secondly, the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct bytes. Finally, an intelligent search method is proposed to identify optimal parameters, further improving attack efficiency. Experiments were conducted on both simulated environments and real power traces collected from the SAKURA-G platform. In the case of simulation, SFGA-CPA reduces the number of traces by 27.3% and 60% compared to CPA based on multiple screening methods (MS-CPA) and CPA based on simple GA method (SGA-CPA) when the success rate reaches 90%. Moreover, real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.

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Section: Real Experiments Of Sakura-gmentioning

confidence: 99%

SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

Liu,

Li,

et al. 2024

CMC

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“…As the scale of neurons in the learning phase expands, most existing systematic and non-systematic logical rules employ metaheuristic algorithms as optimization training algorithms, iteratively finding consistent interpretation. The existing work of Zamri et al [11] initially employs a genetic algorithm (GA) as the logic phase and learning phase algorithm for RAN2SAT in the DHNN model, known as DHNN-r2SAT. This model is compared with traditional exhaustive search (ES) and demonstrates superior retrieval performance.…”

Section: Inefficient Learning Phase Of Dhnnsmentioning

confidence: 99%

“…Currently, most systematic or non-systematic logics follow the retrieval phase training mechanism proposed by Abdullah [7], known as the Wan Abdullah (WA) method. For example, the PRO2SAT by Chen et al [8], the 2SAT by Zamri et al [11], and the RANkSAT by Someetheram et al [12] all adopt this approach. These models update neuron states using the hyperbolic tangent activation function (HTAF) after local field computation.…”

Section: Limited Solution Diversity In Retrieval Phase Of Dhnnmentioning

confidence: 99%

“…Currently, more metaheuristic algorithms have been applied to systematic and non-systematic logics of DHNNs. As proposed by [11], the GA algorithm was imbedded in the logic phase and learning phase of DHNNs. During the logic phase, the GA algorithm assists in constructing the logical rules of r2SAT according to the desired ratio of negative literals.…”

Section: Introductionmentioning

confidence: 99%

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MTS-PRO2SAT: Hybrid Mutation Tabu Search Algorithm in Optimizing Probabilistic 2 Satisfiability in Discrete Hopfield Neural Network

Chen,

Gao,

Kasihmuddin

et al. 2024

Mathematics

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The primary objective of introducing metaheuristic algorithms into traditional systematic logic is to minimize the cost function. However, there is a lack of research on the impact of introducing metaheuristic algorithms on the cost function under different proportions of positive literals. In order to fill in this gap and improve the efficiency of the metaheuristic algorithm in systematic logic, we proposed a metaheuristic algorithm based on mutation tabu search and embedded it in probabilistic satisfiability logic in discrete Hopfield neural networks. Based on the traditional tabu search algorithm, the mutation operators of the genetic algorithm were combined to improve its global search ability during the learning phase and ensure that the cost function of the systematic logic converged to zero at different proportions of positive literals. Additionally, further optimization was carried out in the retrieval phase to enhance the diversity of solutions. Compared with nine other metaheuristic algorithms and exhaustive search algorithms, the proposed algorithm was superior to other algorithms in terms of time complexity and global convergence, and showed higher efficiency in the search solutions at the binary search space, consolidated the efficiency of systematic logic in the learning phase, and significantly improved the diversity of the global solution in the retrieval phase of systematic logic.

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“…This section details the experimental design and analysis aimed at assessing the efficacy of the proposed scheme [ 39 ]. Specifically, it covers the simulation platform used, the algebraic performance metrics utilized, a brief discussion of the algebraic analysis results, the Friedman test used to validate the superiority of the proposed model compared to other S-box models and limitations of the proposed scheme for S-box construction.…”

Section: Experimental Setup and Analysismentioning

confidence: 99%

Secure communication through reliable S-box design: A proposed approach using coset graphs and matrix operations

Razaq

Alhamzi

Abbas

et al. 2023

Heliyon

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Weighted Random k Satisfiability for k=1,2 (r2SAT) in Discrete Hopfield Neural Network

Cited by 75 publications

References 33 publications

SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

MTS-PRO2SAT: Hybrid Mutation Tabu Search Algorithm in Optimizing Probabilistic 2 Satisfiability in Discrete Hopfield Neural Network

Secure communication through reliable S-box design: A proposed approach using coset graphs and matrix operations

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