Quantum-Inspired Evolutionary Algorithms for Adaptive Intrusion Detection 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

Intrusion Detection Systems (IDS) has an important function to networks through efficient protection against unauthorized access as well as computer violations. To be more specific, there is no indication that traditional IDS applications can be hampered in terms of nimbleness, given that the security threats context is in a rather continuous state of flux. With the help of such methods as QIEAs, which is a comparatively new evolutional algorithm elaborated through the incorporation of the notions of quantum computing, it is possible to envisage an idea of an introduction of an evolutionary change to the intensification and the extension of flexibility and efficiency of IDS. The article under discussion covers the use of QIEAs in improving adaptive IDS and its strengths, weaknesses, and research prospects.

Introduction

Intrusion Detection Systems (IDS) plays a critical link in the area of computer networks for purposes mainly of surveillance and protection of computer networks from any form of intrusion [1]. Thus, IDS has to remain active to detect a large number of threats, display high detection rates and response time as threats in the cyberspace evolve [2]. Other possible solution is to consider special Quantum-inspired evolutionary algorithms (QIEAs) as the part of the concept based on using the ideas belonging to the field of quantum computing for enhancement of IDS flexibility and productivity [3]. Presently, many organizations face network security attacks and insecure data protection, on the other hand, IDSs are being designed with the capability of incorporating response mechanisms for the detection of such attacks [4]. This article presents the concept of QIEAs, the method in which they are applied in adaptive IDS and the issues arising from it.

Quantum-inspired evolutionary algorithms

Quantum-inspired evolutionary algorithms (QIEAs) are formed through the integration of concepts that are inherent in quantum computing for instance superposition and entanglement with the conventional evolutionary algorithms for the purpose of improving the search and optimization [3]. It turns out from the fact that these algorithms are based on the quantum bits or, to be more precise, on the qubits, which allows working with many states at a time, which in its turn helps to search for more solutions in the given problem space [5].

  • Quantum Genetic Algorithms (QGA):

Quantum Genetic Algorithms (QGAs) combine quantum processes with standard genetic algorithms, allowing for a more comprehensive and efficient search for optimum solutions. There is a type of optimization the of QGAs that combine quantum procedures with the standard genetic algorithms to do even more extensive and efficient searches of the best solutions. QGAs utilize quantum superposition that assists the tool to assess various possible solutions at once, meaning it enhances a search’s efficiency [3], [6].

Application in Adaptive IDS-

QGAs may optimize several parameters in adaptive IDS, including detection thresholds, rule sets, and response techniques. System decision makers involved in the design of adaptive IDS may adjust various parameters and characteristics of the IDS such as the general thresholds, rules or else, modes of operation. The dynamic optimization capacity helps IDS in dealing with the newer threats and changes, which are sort of, happened in the environment with better detection precision and flexibility [5].

  • Quantum Inspired Particle Swarm Optimization (QPSO):

Quantum-Inspired Particle Swarm Optimization (QPSO) expands upon classical particle swarm optimization by applying quantum mechanics concepts. This enables particles to more effectively explore the solution space, resulting in faster convergence and better optimization performance [7].

Application in Adaptive IDS-

In adaptive IDS, QPSO may optimize parameters including sensor location, data aggregation algorithms, and anomaly detection thresholds [7]. By constantly altering these settings, QPSO improves the IDS’s capacity to identify and respond to threats in real time.

Adaptive intrusion detection systems

Adaptive Intrusion Detection Systems (IDS) are intended to change their detection and response methods in response to the changing threat landscape [1]. This flexibility is critical for maintaining high detection accuracy while reducing false positives [2]. Figure 1 shows about the Adaptive Intrusion Detection Systems.

Figure 1: Adaptive Intrusion Detection Systems


Advantages of QIEAs in Adaptive IDS-

  • Enhanced Adaptability: This makes complexity easier for IDS to confront new and evolving threats that enhances the ability of IDS to detect accurately [3].
  • Improved Efficiency: In the other words, The QIEAs reduce the computational cost of the search and optimization which is discussed in terms of the quantum computing [5].
  • Scalability: Certain of the QIEAs may affect to large scale network systems where solutions can be gotten by generalizing to answering the questions of security that are difficult to answer [7].

Challenges of Implementing QIEAs

  • Computational Complexity:

Regarding QIEAs, while it is about efficiency, it faces the problem of big computation due to the involvement of the complicated quantum process [3]. A traditional problem when working in IDS in real time is that the computation time of a specific program and the level of security are always in conflict [5].

  • Quantum-Specific Knowledge:

QIEAs need to be done by the representatives of specialists in the field of quantum computing and, in specific, in the Evolutionary Algorithm field. This is a challenge to many businesses since they have to have correct training as well as adequate experience which they will have to have in the implementation of the objectives [7].

  • Integration of Existing Systems:

It also becomes challenging to implement QIEAs because specific structures like the IDS are hard to establish in organizations in order to maintain high quality. Ensure compatibility and flawless operation to maximize their benefits [2].

Emerging trends and future directions

  • Hybrid Quantum–Classical Approaches:

Combining quantum-inspired algorithms with conventional approaches can improve their efficiency and help solve computing problems. Hybrid techniques use the qualities of both paradigms to deliver strong security solutions [7].

  • Quantum Machine Learning:

Both the methods of quantum-inspired evolutionary algorithms and machine learning can be applied successively to enhance the danger identification and management. Thus, the machine learning quantum has flexibility in the detection and prevention of risks to security beyond traditional methods [3].

Case Studies & Practical Applications

  • Case Study #1: Financial Sector:

Financial sector to minimize the occurrence and impact of fraud. Adaptive IDS has also proved to be beneficial in the transaction systems within the banking Industry through use of QIEAs that assist in identifying the various complex cyber-attacks and counteracting them. Based from the paper, IDS detection accuracy and reaction time intensified due to dynamics optimization and the probability of Financial Fraud successfully minimized [7].

  • Case Study #2: Healthcare Industry:

Financial sector to minimize the occurrence and impact of fraud. Adaptive IDS has also proved to be beneficial in the transaction systems within the banking Industry through use of QIESA that assist in identifying the various complex cyber-attacks and counteracting them. Based from the paper, IDS detection accuracy and reaction time intensified due to dynamics optimization and the probability of Financial Fraud successfully minimized [2].

Conclusion

As there is the evolution in the way of thinking of the nowadays used evolutionary algorithms it is now possible to have the strengthened possibility to develop adaptive IDSs. What is more, the algorithms introduced here are more flexible, have better computational complexity, and are less vulnerable to failure caused by the usage of the principles derived from the quantum computing paradigms. These concerns have been answered to some extent for smart farming in various works using different security solutions namely Green-IoT-based agriculture through blockchain for privacy, authentication and access control for smart farming, symmetric data encryption between agricultural sensors, intrusion detection system, and physical security countermeasures among others. Nevertheless, the present implementation of biometric combines necessary preliminary data and is effective in spite of present implementation obstacles as a relatively additional utility in enhancing the IDS capability of an organization. These will in all probability be improved in the future by more advancement in future Hybrid approaches and quantum machine learnings.

References

  1. R. Bace and P. Mell, “Intrusion Detection Systems,” NIST, Nov. 2001, Accessed: Jul. 13, 2024. [Online]. Available: https://www.nist.gov/publications/intrusion-detection-systems
  2. D. E. Denning, “An Intrusion-Detection Model,” IEEE Trans. Softw. Eng., vol. SE-13, no. 2, pp. 222–232, Feb. 1987, doi: 10.1109/TSE.1987.232894.
  3. 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.
  4. P. Pappachan, Sreerakuvandana, and M. Rahaman, “Conceptualising the Role of Intellectual Property and Ethical Behaviour in Artificial Intelligence,” in Handbook of Research on AI and ML for Intelligent Machines and Systems, IGI Global, 2024, pp. 1–26. doi: 10.4018/978-1-6684-9999-3.ch001.
  5. 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.
  6. “Understanding the Metaverse: Security & Forensics Book Chapter | IGI Global.” Accessed: Oct. 04, 2024. [Online]. Available: https://www.igi-global.com/chapter/understanding-the-metaverse/354644
  7. 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.
  8. Aldweesh, A., Alauthman, M., Al Khaldy, M., Ishtaiwi, A., Al-Qerem, A., Almoman, A., & Gupta, B. B. (2023). The meta-fusion: A cloud-integrated study on blockchain technology enabling secure and efficient virtual worlds. International Journal of Cloud Applications and Computing (IJCAC), 13(1), 1-24.
  9. M. Casillo, F. Colace, B. B. Gupta, A. Lorusso, F. Marongiu and D. Santaniello, “Blockchain and NFT: a novel approach to support BIM and Architectural Design,” 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), Sakheer, Bahrain, 2022, pp. 616-620, doi: 10.1109/3ICT56508.2022.9990815.
  10. P. Chaudhary, B. B. Gupta, K. T. Chui and S. Yamaguchi, “Shielding Smart Home IoT Devices against Adverse Effects of XSS using AI model,” 2021 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 2021, pp. 1-5, doi: 10.1109/ICCE50685.2021.9427591.

Cite As

Katiyar A. (2024) Quantum-Inspired Evolutionary Algorithms for Adaptive Intrusion Detection Systems, Insights2Techinfo, pp.1

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