Substitution boxes(S-boxes)are key components of symmetrical cryptosystems,acting as nonlinear substitutionfunctions that hide the relationship between the encrypted text and input key.This confusion mechanism is vita...Substitution boxes(S-boxes)are key components of symmetrical cryptosystems,acting as nonlinear substitutionfunctions that hide the relationship between the encrypted text and input key.This confusion mechanism is vitalfor cryptographic security because it prevents attackers from intercepting the secret key by analyzing the encryptedtext.Therefore,the S-box design is essential for the robustness of cryptographic systems,especially for the dataencryption standard(DES)and advanced encryption standard(AES).This study focuses on the application of theffreffy algorithm(FA)and metaheuristic lion optimization algorithm(LOA),thereby proposing a hybrid approachcalled the metaheuristic lion ffreffy(ML-F)algorithm.FA,inspired by the blinking behavior of ffreffies,is a relativelynew calculation technique that is effective for various optimization problems.However,FA offen experiences earlyconvergence,limiting the ability to determine the global optimal solution in complex search areas.To address thisproblem,the ML-F algorithm was developed by combining the strengths of FA and LOA.This study identiffesa research gap in enhancing S-box nonlinearity and resistance to differential attacks,which the proposed ML-Faims to address.The main contributions of this paper are the enhanced cryptographic robustness of the S-boxesdeveloped with ML-F,consistently outperforming those generated by FA and other methodsregarding nonlinearityand overall cryptographic properties.The LOA,inspired by the social hunting behavior of lions,uses the collectiveintelligence of a pride of lions to explore and exploit the search space more effectively.The experimental analysis ofthisstudy focused on the main encryption criteria,namely,nonlinearity,the bit independence criterion(BIC),strictavalanche criterion(SAC),differential probability(DP),and maximum expected linear probability(MELP).Thesecriteria ensure that the S-boxes provide robust security against various cryptanalytic attacks.The ML-F algorithmconsistently surpassed the FA and other optimization algorithms in generating S-boxes with higher nonlinearityand better overall cryptographic properties.In case of ML-F-based S-boxes,the results indicated a better averagenonlinear score and more resistance against several cryptographic attacks for quite a number of criteria.Therefore,they were considered more reliable while dealing with secured encryption.The values generated by the ML-FS-boxes are near ideal in both SAC and BIC,indicating better diffusion properties and consequently,enhancedsecurity.The DP analysisfurthershowed that the ML-F-generated S-boxes are highly resistant to differential attacks,which is a crucial requirement for secure encryption systems.展开更多
文摘Substitution boxes(S-boxes)are key components of symmetrical cryptosystems,acting as nonlinear substitutionfunctions that hide the relationship between the encrypted text and input key.This confusion mechanism is vitalfor cryptographic security because it prevents attackers from intercepting the secret key by analyzing the encryptedtext.Therefore,the S-box design is essential for the robustness of cryptographic systems,especially for the dataencryption standard(DES)and advanced encryption standard(AES).This study focuses on the application of theffreffy algorithm(FA)and metaheuristic lion optimization algorithm(LOA),thereby proposing a hybrid approachcalled the metaheuristic lion ffreffy(ML-F)algorithm.FA,inspired by the blinking behavior of ffreffies,is a relativelynew calculation technique that is effective for various optimization problems.However,FA offen experiences earlyconvergence,limiting the ability to determine the global optimal solution in complex search areas.To address thisproblem,the ML-F algorithm was developed by combining the strengths of FA and LOA.This study identiffesa research gap in enhancing S-box nonlinearity and resistance to differential attacks,which the proposed ML-Faims to address.The main contributions of this paper are the enhanced cryptographic robustness of the S-boxesdeveloped with ML-F,consistently outperforming those generated by FA and other methodsregarding nonlinearityand overall cryptographic properties.The LOA,inspired by the social hunting behavior of lions,uses the collectiveintelligence of a pride of lions to explore and exploit the search space more effectively.The experimental analysis ofthisstudy focused on the main encryption criteria,namely,nonlinearity,the bit independence criterion(BIC),strictavalanche criterion(SAC),differential probability(DP),and maximum expected linear probability(MELP).Thesecriteria ensure that the S-boxes provide robust security against various cryptanalytic attacks.The ML-F algorithmconsistently surpassed the FA and other optimization algorithms in generating S-boxes with higher nonlinearityand better overall cryptographic properties.In case of ML-F-based S-boxes,the results indicated a better averagenonlinear score and more resistance against several cryptographic attacks for quite a number of criteria.Therefore,they were considered more reliable while dealing with secured encryption.The values generated by the ML-FS-boxes are near ideal in both SAC and BIC,indicating better diffusion properties and consequently,enhancedsecurity.The DP analysisfurthershowed that the ML-F-generated S-boxes are highly resistant to differential attacks,which is a crucial requirement for secure encryption systems.
