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@article{200017,
        author = {AKSHADA PATIL and Dr.Nilesh Thorat and Prashant rode and keyur patle and abhinav pal},
        title = {Quantum-Inspired Machine Learning for Intrusion Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {3836-3841},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200017},
        abstract = {Modern cyber threats are growing in both volume and complexity, exposing the limitations of rule-based network monitoring tools. This work proposes a dual-model intrusion detection framework that pairs a classical Support Vector Ma-chine (SVM) with a quantum-inspired counterpart built on Qiskit’s kernel estimation routines. Both classifiers are trained and tested on the NSL-KDD benchmark to distinguish benign traffic from malicious activity. Evaluation outcomes confirm that the classical model delivers strong, consistent detection performance, while the quantum-inspired variant demonstrates feasibility of quantum kernel computation within a simulation environment. Rather than claiming performance superiority, this study positions quantum-inspired methods as a promising avenue for next-generation security systems, offering practitioners and researchers a reproducible starting point for future hardware-level quantum IDS development.},
        keywords = {Intrusion Detection System, Machine Learning, Quantum-Inspired Computing, Support Vector Machine, NSL-KDD, Qiskit, Cybersecurity, Network Security},
        month = {May},
        }