Due to their significant correlation and redundancy,conventional block cipher cryptosystems are not efficient in encryptingmultimedia *** based onCellularAutomata(CA)can provide amore effective *** CA have recently ga...
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Due to their significant correlation and redundancy,conventional block cipher cryptosystems are not efficient in encryptingmultimedia *** based onCellularAutomata(CA)can provide amore effective *** CA have recently gained recognition as a robust cryptographic primitive,being used as pseudorandom number generators in hash functions,block ciphers and stream *** have the ability to perform parallel transformations,resulting in high throughput ***,they exhibit a natural tendency to resist fault *** stream cipher schemes based on CA have been proposed in the ***,their encryption/decryption throughput is relatively low,which makes them unsuitable formultimedia *** and Grain are efficient stream ciphers that were selected as finalists in the eSTREAM project,but they have proven to be vulnerable to differential fault *** work introduces a novel and scalable stream cipher named CeTrivium,whose design is based on *** is a 5-neighborhood CA-based streamcipher inspired by the designs of Trivium and *** is constructed using three building blocks:the Trivium(Tr)block,the Nonlinear-CA(NCA)block,and the Nonlinear Mixing(NM)*** NCA block is a 64-bit nonlinear hybrid 5-neighborhood CA,while the Tr block has the same structure as the Trivium stream *** NM block is a nonlinear,balanced,and reversible Boolean function that mixes the outputs of the Tr and NCA blocks to produce a *** of CeTrivium has indicated that it can resist various attacks,including correlation,algebraic,fault,cube,Meier and Staffelbach,and side channel ***,the scheme is evaluated using histogramand spectrogramanalysis,aswell as several differentmeasurements,including the correlation coefficient,number of samples change rate,signal-to-noise ratio,entropy,and peak signal-to-noise *** performance of CeTrivium is evaluated and compared with other state-of-
Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomef...
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Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomefamous due to its high tendency towards convergence to the global optimummost of the time. But, still the standard bat with random walk has a problemof getting stuck in local minima. In order to solve this problem, this researchproposed bat algorithm with levy flight random walk. Then, the proposedBat with Levy flight algorithm is further hybridized with three differentvariants of ANN. The proposed BatLFBP is applied to the problem ofinsulin DNA sequence classification of healthy homosapien. For classificationperformance, the proposed models such as Bat levy flight Artificial NeuralNetwork (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) arecompared with the other state-of-the-art algorithms like Bat Artificial NeuralNetwork (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distributionback propagation (BatGDBP), in-terms of means squared error (MSE) andaccuracy. From the perspective of simulations results, it is show that theproposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185,and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on *** on WL10 the proposed BatLFANN achieved 99.89899% accuracy withMSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853%accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracywith MSE of 0.006738 which achieve better accuracy as compared to the otherhybrid models.
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