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@article{168866,
        author = {Deepak Kaul},
        title = {Dynamic AI-Based Intrusion Detection for Quantum Computing Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {9},
        number = {10},
        pages = {1068-1073},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168866},
        abstract = {As quantum computing continues to advance, traditional intrusion detection systems (IDS) may prove inadequate in protecting against quantum-level cybersecurity threats. This paper proposes a novel AI-based intrusion detection system (AI-IDS) specifically designed for quantum computing networks (QCN). By leveraging machine learning (ML) algorithms and quantum data patterns, we develop an adaptive and dynamic framework capable of detecting irregularities unique to quantum communication protocols. Our model integrates classical and quantum features to provide comprehensive protection against both classical and quantum threats. Initial results demonstrate that the proposed AI-IDS can effectively identify quantum-level anomalies with high accuracy, outperforming conventional IDS in a quantum environment. The implications of this work are significant, as quantum networks will be fundamental to the next generation of secure communication systems, and there is limited research focusing on the cybersecurity risks within these networks. This research highlights the importance of early interventions in quantum cybersecurity and sets the foundation for further exploration of AI-based solutions in the context of quantum computing.},
        keywords = {Quantum Computing Networks, Intrusion Detection, Cybersecurity, Machine Learning, Quantum Anomalies, AI-Based Detection, Quantum Communication Protocols},
        month = {October},
        }