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@article{194700,
        author = {Appadi Geethika and Ashamolla Harshika and Vippari Neha and Dr. Potu Narayana},
        title = {Hybrid Quantum-Classical Intrusion Detection System for Next-Gen Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {5216-5221},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194700},
        abstract = {The current state of computer networks today experiences severe cybersecurity threats which include unauthorized access and malware attacks and data breaches. The detection of complex patterns through traditional intrusion detection systems faces challenges because they cannot handle the detection process which needs to analyze network traffic in modern network environments. The proposed research solution establishes a Quantum– Classical Intrusion Detection System which the researchers designed for next-gen network systems. The system uses network flow data to implement feature extraction and classification mechanisms which identify malicious traffic patterns. The hybrid quantum–classical model improves detection capability by combining the efficiency of algorithms with the advanced potential of quantum methods. The proposed system protects environments through enhanced security measures which increase threat detection accuracy and establish a dependable protection system for next-gen network environments against emerging cyber threats.},
        keywords = {Intrusion Detection System, QuantumClassical Computing, Network Security, Cyber Attack Detection, UNSW-NB15 Dataset, Machine Learning, Network Traffic Analysis, Cybersecurity Monitoring, Anomaly Detection, Artificial Intelligence, Distributed Systems, Next- Generation Networks.},
        month = {March},
        }