Research

Quantum Genetic Algorithms for Optimizing Cybersecurity Encryption Systems

By: Achit Katiyar1,2

1South Asian University, New Delhi, India.

2International Center for AI and Cyber Security Research and Innovations, Asia University, Taiwan. Email: achitktr@gmail.com

Abstract: The growth of threats in the cyber world is relatively fast, and hence there is a need for efficient and effective encryption techniques. Conventional encryption methods may be efficient today, but they are highly susceptible to the quantum computing challenge; therefore, Quantum Genetic Algorithms (QGA) improves the optimization of cryptographic protocols by integrating the principles of quantum computing and GA. This paper introduce how GA-based approach relates to enhancing the cybersecurity encryption systems.

Introduction:

Quantum genetic algorithms are considered to be one of the best solutions in the context of application to improve the cybersecurity encryption systems based on the strengths of the quantum genetic algorithms as well as the classical ones. This synthesis helps in improving key generation and the process of encryption with an improvement in the level of security.

Encryption is dynamic in maintaining data confidentiality, integrity and authenticity, however, with the emergence of quantum computing the current encryption platforms are at risk [1]. RSA and AES encryption algorithms belong to classical cryptography that relies on number theory and those functions known to be complicated by mathematicians include factorization of large numbers and discrete logarithms [2]. However, there are such algorithms as Shor’s algorithm of factorization, which works in the quantum terms, and it is beyond any doubt that it solves the problem significantly faster than the classical variant [3]. This is a major threat to most of the currently employed public-key encryption techniques.

Genetic Algorithms (GAs), which adopted the mechanism of natural selection, and have been employed in solution formulation of optimization problems for quite a long time, have application in different domains comprising cryptography [4]. Quantum Genetic Algorithms (QGAs) extend this idea to incorporate actual quantum features such as superposition for the purpose of expanding the search across the solution space [5]. This facilitates the optimization process because there are diverse parameters that have to be adjusted in the case of encryption system design for reasons of security and performance [6].

Background of Quantum Genetic Algorithms (QGA):

Quantum Genetics Algorithm or QGA are basically a variation of genetic algorithms used commonly in optimization problems. New with quantum computing is the notion of a qubit, which anticipates a value both a classical bit shall take, as it can be in two states at once [7]. This helps the quantum computers compute through several possible solutions at once. In QGAs operators, qubits act like potential solutions, and hence More solution space than classical GAs can be searched through using quantum parallelism [8]. In case of cybersecurity, it can be used to improve on encryption systems by using optimization algorithms that determine the best possible settings for the systems that are both secure and not very much demanding computationally [9].

Use of Quantum Genetic Algorithm for the Optimization of Encryption Systems:

Problem Definition:

An encryption system often has a few parameters associated with it that define its security and efficiency, such as key size, algorithm sophistication and the rate of encryption and decryption [10]. These parameters should be optimized; this becomes even important in the post-quantum world, where traditional key-based cryptosystems could be efficiently attacked using quantum techniques [1].



QGA Process:

The following steps explain the procedure of Quantum Genetic Algorithm employed for the optimization of the key parameters of encryption. These steps are adapted from general principles of genetic algorithms with modifications that incorporate quantum computing elements [11]:

  • Initialization: A quantum population is created, in which every individual is described with a qubit that is in the state of a quantum superposition. This makes it possible to prepare all possible new encryption solutions at once.
  • Quantum Superposition: All qubits are put to initial state one which effectively means that all of the units are aspiring to show multiple solutions at once by application of quantum parallelism [12].
  • Evaluation: In each case, the suitability is assessed as security and efficiency of the applied encryption system.
  • Quantum Mutation and Crossover: The mutation and crossover operations are performed with the intent to increase the chances of the algorithm to explore the solution space and avoid getting stuck in low quality solutions.
  • Selection: The best adapted specimens reproduce obtaining that the population evolves to the optimal encryption parameters.
  • Convergence: The process continues until an optimal set of parameters that satisfies the required amount of security necessary to prevent unauthorized access while also achieving the maximum performance possible is achieved the process continues.
Figure 1: Illustrative Algorithm of QGA for optimizing encryption systems

Conclusion:

Quantum Genetic Algorithms can be considered as a perspective method in the domain of optimization of encryption systems with the help of the potentialities of quantum computing. With the help of quantum superposition and quantum entanglement, QGAs can find solutions to problems in a large solution space than those which are applicable in classical computation. This makes them particularly suitable in improving cryptographic systems to be more secure against quantum attacks. QGAs will become an essential component of protecting digital assets. In the future, as the field of quantum computing develops, QGAs will become even more important for maintaining the security of data.

References:

  1. D. J. Bernstein, “Introduction to post-quantum cryptography,” in Post-Quantum Cryptography, D. J. Bernstein, J. Buchmann, and E. Dahmen, Eds., Berlin, Heidelberg: Springer, 2009, pp. 1–14. doi: 10.1007/978-3-540-88702-7_1.
  2. R. L. Rivest, A. Shamir, and L. Adleman, “A method for obtaining digital signatures and public-key cryptosystems,” Commun ACM, vol. 21, no. 2, pp. 120–126, Feb. 1978, doi: 10.1145/359340.359342.
  3. P. W. Shor, “Algorithms for quantum computation: discrete logarithms and factoring,” in Proceedings 35th Annual Symposium on Foundations of Computer Science, Nov. 1994, pp. 124–134. doi: 10.1109/SFCS.1994.365700.
  4. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 – International Conference on Neural Networks, Nov. 1995, pp. 1942–1948 vol.4. doi: 10.1109/ICNN.1995.488968.
  5. A. Mansori and S. Nguyeni, “Quantum-Inspired Genetic Algorithms for Combinatorial Optimization Problems,” Algorithm Asynchronous, vol. 1, pp. 16–23, Aug. 2023, doi: 10.61963/jaa.v1i1.47.
  6. K.-H. Han and J.-H. Kim, “Quantum-inspired evolutionary algorithm for a class of combinatorial optimization,” IEEE Trans. Evol. Comput., vol. 6, no. 6, pp. 580–593, Dec. 2002, doi: 10.1109/TEVC.2002.804320.
  7. M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information. Cambridge University Press, 2000.
  8. M. R. Garey and D. S. Johnson, Computers and Intractability: A Guide to the Theory of NP-completeness. Freeman, 1979.
  9. C.-W. Tsai, S.-P. Tseng, M.-C. Chiang, C.-S. Yang, and T.-P. Hong, “A High-Performance Genetic Algorithm: Using Traveling Salesman Problem as a Case,” Sci. World J., vol. 2014, p. 178621, 2014, doi: 10.1155/2014/178621.
  10. M. Rehman, “Quantum-enhanced Chaotic Image Encryption: Strengthening Digital Data Security With 1-D Sine-based Chaotic Maps and Quantum Coding,” J. King Saud Univ. – Comput. Inf. Sci., vol. 36, p. 101980, Feb. 2024, doi: 10.1016/j.jksuci.2024.101980.
  11. A. Narayanan and M. Moore, “Quantum-inspired genetic algorithms,” in Proceedings of IEEE International Conference on Evolutionary Computation, May 1996, pp. 61–66. doi: 10.1109/ICEC.1996.542334.
  12. “Quantum-Resistant Cryptography: Security & Forensics Book Chapter | IGI Global.” Accessed: Oct. 04, 2024. [Online]. Available: https://www.igi-global.com/chapter/quantum-resistant-cryptography/354037

Cite As

Katiyar A. (2024) Quantum Genetic Algorithms for Optimizing Cybersecurity Encryption Systems, Insights2Techinfo, pp.1

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