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 provided quantum-inspired evolutionary algorithms (QIEAs) can be used as a new approach to increase security and adaptability of Multi-factor Authentication (MFA) systems according to the principles of quantum algorithms. The approaches used in MFA solutions of the older generation have some drawbacks in maintaining the performance and flexibility in the context of the steady development of threats.
Introduction
Of all the elements of the modern approach to security, multi-factor authentication (MFA) can be considered as one of the most important, as this system significantly broadens the application of the basic username and password [1]. MFA, a security system that demands multiple factors of identification from non-interdependent classes of factors, is significant in IioT [2]. In its execution, the common MFA deployment process normally involves something; the user knows (password), something the user has (token or smartphone), and something the user is (biometric scan) [3]. However, these systems needed to be upgraded as the risk of cyber security increased over time. The possibility of enhancement in the QIEAs using the various concept of quantum computing to former increase the reliability and adaptability of the systems of MFA.
Quantum-inspired evolutionary algorithms
Quantum-inspired evolutionary algorithms is one of the metaheuristic optimization algorithms that is achieved from applying the principles of quantum computing such as superposition and entanglement to the conventional evolutionary algorithms [4]. These algorithms exploit the concept of quantum computing in which a thing can be in many states at the same time thereby searching for the highest possible solutions [5].
- Quantum Genetic Algorithms (QGA):
In general, Quantum Genetic Algorithms are a combination of the quantum principles and common genetic algorithms making the search of the solution more effective. QGAs can use quantum superposition, that is, several potential answers at the same time, hence the search speed could be higher by several times [4], [6].
Application in MFA systems-
QGAs could improve several aspects of MFA for instance; the authentication thresholds, the match algorithms of biometrics or the methods of generating tokens. This dynamic optimization capacity enables the MFA systems to meet the newly emerging and evolving threats hence improving on the aspects of security and flexibility [4].
- Quantum Inspired Particle Swarm Optimization (QPSO):
Quantum computing principles integrates to the classical PSO and develop the Quantum-inspired particle swarm optimization (QPSO). This lets the particles get a better region of the solution space and optimize faster hence better performance [7].
Application in MFA systems-
It can therefore be an implication that QPSO can be used in the optimisation of the MFA factors for instance; the biometric templates; the synchronisation period of the token; and the methods applied in modelling the users’ behaviour. Thus, by altering these setting in the course of real-time functioning, QPSO increases the effectiveness of MFA system in addressing these irregularities. [7].
Implementation Methodology
1. The selection of the framework that was used and the quantum adaptation
- Choose a classical evolutionary algorithm:
(a) First of all, it is necessary to define, which specific type of the evolutionary algorithms is to be chosen, for instance, Genetic Algorithms (GA) or Differential Evolution (DE).
(b) Let us take an example of simple GA.
- Integrate Quantum Mechanics principles:
(a) Quantum Representation: Quantum bits (qubits) are used instead of classical bits to represent the solutions. Each qubit can exist in a superposition of states, and providing a better range in search space.
(b) Quantum Gates: Perform the quantum gates (e.g., Hadamard gate) in the manipulation qubits and explore multiple states simultaneously.
(c) Quantum Mutation and Crossover: Adjust classical GA operations (mutation and crossover) to the quantum realm to maintain diversity and improve convergence.
2. The Multi Factor Authentication System conception
- Authentication Factors:
(a) Implementation of many authentication factors, such as passwords, biometrics (fingerprints, facial recognition), smart cards, and one-time tokens.
(b) Each factor is assigned a weight based on its security level and user convenience.
- Quantum-Inspired evolutionary process:
(a) Initialize Population: For each of the individuals in the population represents a number of authentication factors, that will be described by a quantum state.
(b) Fitness Function: Therefore, it is necessary to define the measure for evaluating the level of security and convenience of each combination. Some of the aspects that are taken under consideration while defining this function are attack resistance and, obviously, usability.
(c) Quantum Evolution: In view of QIEA, the quantum crossover, mutation, and selection are done on the population to improve the population, in the best possible way, regarding the combination of the authentication fact.
3. Simulation Environment Development
- Experimental structure:
(a) Development allows one to use a particular language like the Python, MATLAB and the likes so as to develop the simulation environment.
(b) Other resource libraries which can be also used are Qiskit that is used for quantum computing, and DEAP for the evolutionary algorithms.
- Data Collection: Collection of diverse datasets, including various user behaviours and attack scenarios, to test the MFA system.
4. Algorithm Implementation and System Integration
- Quantum-Inspired Genetic Algorithm (QIGA):
(a) Quantum Initialization: Randomly
Initialize the population with quantum states representing different combinations of authentication factors.
(b) Quantum Operations: This technique uses the crossover and the mutation to come up with new offspring solution using the quantum crossover factor.
(c) Fitness Evaluation: Therefore, the given fitness function shall evaluate the fitness level of any of the individual that is available in the actual population.
(d) Selection: Then the result of this process is about individual that should be able to reproduce easily the next generation easily.
- Integration with MFA System:
(a) Integrate QIGA module to connect the MFA system so that the MFA system can cross from one factor to another or decide on which factor should be employed.
(b) Ensure that there is proper communication of the QIGA module with other parts that compromise the MFA system.
5. Testing and Validation
- Performance Metrics:
- Authentication Accuracy: Oversee on the read accuracy of the authentication mechanism.
- Computational Efficiency: Evaluation of the time complexity and resource utilization of the QIGA.
- Security Robustness: Assess the system’s resilience against common attacks, such as brute force, phishing, and man-in-the-middle attacks.
- Testing criteria:
- Create testing criteria to validate the performance of system under various conditions, including different user loads and attack simulations.
6. Data Analysis and Visualization
- Statistical Analysis:
- Analyse the collected performance data of the various softwares, statistically.
- Derive the overall conclusion of the QIEA-based MFA system results to that of greater MFA systems.
- Visualization: In order to control the relation between outside data and inside information that is necessary to construct graphs and chart that will show the dynamics of the performance indicators and trends on it.
Robust multi-factor authentication systems
A well implemented MFA is expected to be supplied with the changes in the methodology and the implementation of the identification and confirmation processes concerning Interloper threats [1]. This is to ensure higher security is provided and at the same time, reduce on the figures of false positives [3]. About Robust Multi-Factor Authentication Systems is shown in figure 1.
- Advantages of QIEAs in MFA Systems:
- Enhanced Adaptability: Therefore, as a result of applying QIEAs on MFA systems, it is effective to react on the newly appeared threats, and, as a consequence, improve the safety and reliability of the systems [4].
- Improved Efficiency: Therefore, the QIEAs enhance the speed of the search and optimization actions and reserve the entire calculations under the conception of quantum computing [5].
- Scalability: Therefore, in large scale authentication context, it becomes possible to formulate the QIEAs in the context that offers solutions to numerous security threats [7].

Challenges in Implementing QIEAs
- Computational complexity:
From the given results of this study it can be concluded that QIEAs are more efficient regarding the IEAs and at the same time are computationally costly because of quantum procedure [4]. One of the major considerations for the correct equality of computational cost regards the fact that stronger security together with the presented real time MFA systems is achievable at the same cost [5].
- Quantum-Specific Knowledge:
Implementations of QIEAs needs expert methodologies in quantum computing and evolutionary algorithms. This may go down well with several institutions as they may depict the correct training as well as the experience for the application [7].
- Integration with Existing Systems:
Some difficulties of integrating QIEAs with current MFA infrastructure can be described [3]. It is needed to ensure the compatibility and perfect operation to maximize their benefits [1].
Emerging trends and future directions
- Hybrid Quantum-Classical Approaches:
Combination of quantum-inspired algorithms with conventional approaches can improve their efficiency and solve computing problems [7]. Hybrid techniques combine the qualities of both paradigms to produce solid security solutions.
- Quantum machine learning:
Integrating quantum-inspired evolutionary techniques with machine learning may improve risk detection and response capabilities. Quantum machine learning models may exceed traditional methods for recognizing and reducing safety risks [4].
- Quantum Resistant Cryptography:
Creating quantum-resistant cryptographic algorithms with QIEAs can improve the general safety of MFA systems. These algorithms can provide strong defense against quantum computing-based harm, maintaining the integrity and privacy of authentication data [8].
Case Studies & Practical Applications
- Case Study #1: Financial Sector:
It is pointed out that MFA systems secured and used in the financial industry which utilizes QIEAs have the intention of providing coverage to the Internet Banking or any other financial Business transaction. Thus, the dynamic optimization, which QIEA brought to the MFA systems), has increased the security level of the MFA systems, the MFA systems usability, and the minimization of frauds and financial losses connected with the frauds [7].
- Case Study #2: Healthcare Industry:
In the health care sector alone there are comprehensive MFA systems closely linked with the QIEAs as measures to safeguard the patient’s data and where possible ensure the accessibility of the record. QIEAs capacity to react to new and developing threats has improved the security and dependability of healthcare authentication systems, protecting patient confidentiality and integrity [1].
Conclusion
During this process, one of the possible ways in the business of a proper multi-factor authentication could be a topic of the quantum-inspired evolutionary algorithms. These algorithms have cumulatively evolved based on the confusion and variation characteristics of the said quantum computers and are much more flexible, efficient, and adaptable for managing the said security aspects. Some of them are still in question for implementation and the presence of the questions does not necessarily imply that QIEAs are being of no help because they support the on-going efforts to address inefficiency in MFAs. As stated by the authors, improvement of the hybrid methods and such QML applications of the examined methods will further confer improved efficiency in the work.
References
- W. Burr, D. Dodson, and W. Polk, “Electronic Authentication Guideline,” National Institute of Standards and Technology, NIST Special Publication (SP) 800-63 (Withdrawn), Apr. 2006. doi: 10.6028/NIST.SP.800-63v1.0.2.
- “An Enhanced Multi-Factor Authentication and Key Agreement Protocol in Industrial Internet of Things,” Insights2Techinfo. Accessed: Aug. 05, 2024. [Online]. Available: https://insights2techinfo.com/an-enhanced-multi-factor-authentication-and-key-agreement-protocol-in-industrial-internet-of-things/
- L. O’Gorman, “Comparing passwords, tokens, and biometrics for user authentication,” Proc. IEEE, vol. 91, no. 12, pp. 2021–2040, Dec. 2003, doi: 10.1109/JPROC.2003.819611.
- 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.
- 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.
- A. M. Widodo et al., “Quantum-Resistant Cryptography,” in Innovations in Modern Cryptography, IGI Global, 2024, pp. 100–130. doi: 10.4018/979-8-3693-5330-1.ch005.
- J. Sun, B. Feng, and W. Xu, “Particle swarm optimization with particles having quantum behavior,” in Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), Jun. 2004, pp. 325-331 Vol.1. doi: 10.1109/CEC.2004.1330875.
- D. J. Bernstein and T. Lange, “Post-quantum cryptography,” Nature, vol. 549, no. 7671, pp. 188–194, Sep. 2017, doi: 10.1038/nature23461.
- Li, K. C., Gupta, B. B., & Agrawal, D. P. (Eds.). (2020). Recent advances in security, privacy, and trust for internet of things (IoT) and cyber-physical systems (CPS).
- Chaudhary, P., Gupta, B. B., Choi, C., & Chui, K. T. (2020). Xsspro: Xss attack detection proxy to defend social networking platforms. In Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9 (pp. 411-422). Springer International Publishing.
- Gupta, B. B., Gaurav, A., Arya, V., Alhalabi, W., Alsalman, D., & Vijayakumar, P. (2024). Enhancing user prompt confidentiality in Large Language Models through advanced differential encryption. Computers and Electrical Engineering, 116, 109215.
Cite As
Katiyar A. (2024) Quantum-Inspired Evolutionary Algorithms for Robust Multi-Factor Authentication Systems, Insights2Techinfo, pp.1