Quantum-Inspired Machine Learning for Zero-Trust Cybersecurity: A Paradigm beyond Classical Intrusion Detection

Authors

  • Lamia Akter, Mohammad Majharul Islam Jabed, Ahmed Sohaib Khawer, Sharmin Ferdous, Amit Banwari Gupta

Keywords:

Quantum-Inspired Machine Learning, Zero-Trust Security, Intrusion Detection Systems, c, Quantum Algorithms, Artificial Intelligence

Abstract

The increasing sophistication of cyberattack has demonstrated the relatively ineffectiveness of traditional intrusion detection tools in dynamically and decentralized computing systems. Since organizations evolve to a zero-trust architecture, the necessity to adopt more flexible, scalable, and resilient cybersecurity-related mechanisms becomes essential. In this paper, a paradigm shift with the implementation of quantum-inspired machine learning (QIML) in zero-trust cybersecurity models is proposed. In contrast to conventional detectors that use either fixed signatures or anomaly thresholds, QIML uses concepts inspired by quantum mechanics, including feature representation based on superposition and correlation modelling based on entanglement, to provide better detection accuracy and scalability. The challenges of changing attack vectors, adversarial evasion strategies, and high-dimensional data typical of cloud, edge, and IoT environments are all dealt with in the proposed framework. The methodological focus is given to integrating QIML algorithms into the workflow of trust assessment, identity verification, and continuous monitoring in a zero-trust ecosystem. Initial data suggests that the QIML systems can lead to increased accuracy of the intrusion detection, reduce the number of false positives, and allow predictive defenses that are impractical with classical systems. Exploring the overlap between quantum computing ideas, artificial intelligence, and zero trust principles, the present study offers a visionary insight into the development of future cybersecurity systems that will go beyond the limitations of traditional machine learning practices.

DOI: https://doi.org/10.17762/ijisae.v12i20s.7862

 

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Published

24.04.2024

How to Cite

Lamia Akter. (2024). Quantum-Inspired Machine Learning for Zero-Trust Cybersecurity: A Paradigm beyond Classical Intrusion Detection. International Journal of Intelligent Systems and Applications in Engineering, 12(20s), 1070–1080. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7862

Issue

Vol. 12 No. 20s (2024)

Section

Research Article

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