Multi-Objective Evolution of Strong S-Boxes Using Non-dominated Sorting Genetic Algorithm-II and Chaos for Secure Telemedicine

There exist several performance criteria for cryptographically strong substitution boxes which are often conflicting with each other. Constructing S-boxes that satisfy multiple criteria with optimal tradeoffs is one of the challenging tasks for cryptographers. In practice, the existing S-box designi...

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Vydáno v:	IEEE access Ročník 10; s. 1
Hlavní autoři:	Ahmad, Musheer, Alkanhel, Reem, El-Shafai, Walid, Algarni, Abeer D., Abd El-Samie, Fathi E., Soliman, Naglaa F.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Autocorrelation functions Boxes Chaotic communication Chaotic map Cryptography Design optimization Encryption Genetic algorithms Medical imaging Multi-Objective Evolution Multiple criterion Multiple objective analysis Nonlinearity NSGA-II Optimization Pareto optimization Pareto optimum Performance assessment Permutations Sociology Sorting algorithms Statistics Substitution box Telemedicine
ISSN:	2169-3536, 2169-3536
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Abstract	There exist several performance criteria for cryptographically strong substitution boxes which are often conflicting with each other. Constructing S-boxes that satisfy multiple criteria with optimal tradeoffs is one of the challenging tasks for cryptographers. In practice, the existing S-box designing algorithms are used to optimize only a single performance criterion, mainly the nonlinearity, which usually resulted in weak scores of other equally significant criteria. To overcome this problem, a multi-objective optimization based method is presented in this paper which constructs 8×8 S-boxes satisfying multiple criteria of balancedness, high nonlinearity, low differential uniformity, and low auto-correlation. The fulfillment of multiple objectives is done by applying the chaos-assisted non-dominated sorting genetic algorithm-II to evolve S-boxes. The performance assessment of a proposed method and comparative analysis with available optimization based and other state-of-the art algorithms demonstrate its proficiency to generate significantly better S-box solutions with good Pareto-optimal security features. Eventually, the S-boxes with minimum NL of 110, DU as low as 8, and ACF as low as 80, are obtained after the optimization. Furthermore, the obtained Pareto-optimal S-box is utilized to put forward a medical image encryption algorithm for secure telemedicine services. The suggested encryption algorithm makes use of S-box for performing the required permutation and diffusion of images. The encryption performance assessment and comparison analyses validate its effectiveness for securing medical imagery data in telemedicine networks.
AbstractList	There exist several performance criteria for cryptographically-strong substitution boxes (S-boxes), which are often conflicting with each other. Constructing S-boxes that satisfy multiple criteria with optimal tradeoffs is one of the challenging tasks for cryptographers. In practice, the existing S-box design algorithms are used to optimize performance according to a single performance criterion, mainly the nonlinearity, which usually results in weak scores for other equally-significant criteria. To overcome this problem, a multi-objective optimization-based method is presented in this paper. In this method, <tex-math notation="LaTeX">$8 \times 8$ </tex-math> S-boxes are constructed satisfying multiple criteria of balancedness, high nonlinearity, low differential uniformity, and low auto-correlation. Multiple objectives are fulfilled by applying the chaos-assisted non-dominated sorting genetic algorithm-II to introduce the S-boxes. The performance assessment of the proposed method and the comparative analysis with available optimization tools and other state-of-the-art algorithms demonstrate its proficiency in generating significantly-better S-box solutions with good Pareto-optimal security features. Eventually, the S-boxes with minimum nonlinearity (NL) of 110, differential uniformity (DU) as low as 8, and auto-correlation function (ACF) as low as 80 are obtained after the optimization. Furthermore, the obtained Pareto-optimal S-box is utilized to put forward a medical image encryption algorithm for secure telemedicine services. The suggested encryption algorithm uses an S-box to perform the required permutation and diffusion of images. The encryption performance assessment and comparison analyses validate its effectiveness for securing medical imagery data in telemedicine networks. There exist several performance criteria for cryptographically-strong substitution boxes (S-boxes), which are often conflicting with each other. Constructing S-boxes that satisfy multiple criteria with optimal tradeoffs is one of the challenging tasks for cryptographers. In practice, the existing S-box design algorithms are used to optimize performance according to a single performance criterion, mainly the nonlinearity, which usually results in weak scores for other equally-significant criteria. To overcome this problem, a multi-objective optimization-based method is presented in this paper. In this method, [Formula Omitted] S-boxes are constructed satisfying multiple criteria of balancedness, high nonlinearity, low differential uniformity, and low auto-correlation. Multiple objectives are fulfilled by applying the chaos-assisted non-dominated sorting genetic algorithm-II to introduce the S-boxes. The performance assessment of the proposed method and the comparative analysis with available optimization tools and other state-of-the-art algorithms demonstrate its proficiency in generating significantly-better S-box solutions with good Pareto-optimal security features. Eventually, the S-boxes with minimum nonlinearity (NL) of 110, differential uniformity (DU) as low as 8, and auto-correlation function (ACF) as low as 80 are obtained after the optimization. Furthermore, the obtained Pareto-optimal S-box is utilized to put forward a medical image encryption algorithm for secure telemedicine services. The suggested encryption algorithm uses an S-box to perform the required permutation and diffusion of images. The encryption performance assessment and comparison analyses validate its effectiveness for securing medical imagery data in telemedicine networks. There exist several performance criteria for cryptographically strong substitution boxes which are often conflicting with each other. Constructing S-boxes that satisfy multiple criteria with optimal tradeoffs is one of the challenging tasks for cryptographers. In practice, the existing S-box designing algorithms are used to optimize only a single performance criterion, mainly the nonlinearity, which usually resulted in weak scores of other equally significant criteria. To overcome this problem, a multi-objective optimization based method is presented in this paper which constructs 8×8 S-boxes satisfying multiple criteria of balancedness, high nonlinearity, low differential uniformity, and low auto-correlation. The fulfillment of multiple objectives is done by applying the chaos-assisted non-dominated sorting genetic algorithm-II to evolve S-boxes. The performance assessment of a proposed method and comparative analysis with available optimization based and other state-of-the art algorithms demonstrate its proficiency to generate significantly better S-box solutions with good Pareto-optimal security features. Eventually, the S-boxes with minimum NL of 110, DU as low as 8, and ACF as low as 80, are obtained after the optimization. Furthermore, the obtained Pareto-optimal S-box is utilized to put forward a medical image encryption algorithm for secure telemedicine services. The suggested encryption algorithm makes use of S-box for performing the required permutation and diffusion of images. The encryption performance assessment and comparison analyses validate its effectiveness for securing medical imagery data in telemedicine networks.
Author	Alkanhel, Reem Ahmad, Musheer El-Shafai, Walid Algarni, Abeer D. Soliman, Naglaa F. Abd El-Samie, Fathi E.
Author_xml	– sequence: 1 givenname: Musheer orcidid: 0000-0002-4915-9325 surname: Ahmad fullname: Ahmad, Musheer organization: Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India – sequence: 2 givenname: Reem orcidid: 0000-0001-6395-4723 surname: Alkanhel fullname: Alkanhel, Reem organization: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia – sequence: 3 givenname: Walid orcidid: 0000-0001-7509-2120 surname: El-Shafai fullname: El-Shafai, Walid organization: Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt – sequence: 4 givenname: Abeer D. surname: Algarni fullname: Algarni, Abeer D. organization: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia – sequence: 5 givenname: Fathi E. orcidid: 0000-0001-8749-9518 surname: Abd El-Samie fullname: Abd El-Samie, Fathi E. organization: Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt – sequence: 6 givenname: Naglaa F. surname: Soliman fullname: Soliman, Naglaa F. organization: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia
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SubjectTerms	Autocorrelation functions Boxes Chaotic communication Chaotic map Cryptography Design optimization Encryption Genetic algorithms Medical imaging Multi-Objective Evolution Multiple criterion Multiple objective analysis Nonlinearity NSGA-II Optimization Pareto optimization Pareto optimum Performance assessment Permutations Sociology Sorting algorithms Statistics Substitution box Telemedicine
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Title	Multi-Objective Evolution of Strong S-Boxes Using Non-dominated Sorting Genetic Algorithm-II and Chaos for Secure Telemedicine
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