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@article{199114,
        author = {A. Ruba and M.I.Saheedha Sumaya and A. Navrin Fathima and S. Al Rida and S. Al Jameeyathul Sarifa},
        title = {IMPROVING 5G NETWORK SECURITY USING GAN-BASED INTRUSION DETECTION WITH FLASK INTEGRATION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {14518-14525},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199114},
        abstract = {The rapid growth of 5G networks and large-scale communication systems has significantly increased the complexity and volume of network traffic, making traditional security mechanisms insufficient to detect modern cyber threats. Intrusion Detection Systems (IDS) play a crucial role in identifying malicious activities and ensuring network security. However, existing IDS solutions often struggle with detecting zero-day attacks and handling imbalanced datasets.This paper proposes a GAN-based Network Intrusion Detection System (NIDS) integrated with a Flask-based web application for real-time monitoring and prediction. The Generative Adversarial Network (GAN) is utilized to generate synthetic attack data, improving model performance and reducing class imbalance issues. Machine learning algorithms such as Random Forest, Naive Bayes, and K-Nearest Neighbour are applied for classification.
The system is deployed using Flask, enabling user-friendly interaction and real-time intrusion detection. Experimental results demonstrate improved detection accuracy, reduced false alarm rates, and enhanced capability in identifying unknown attacks. The proposed system provides a scalable and efficient solution for securing modern 5G networks.},
        keywords = {Network Intrusion Detection System (NIDS), GAN, 5G Security, Machine Learning, Flask, Cybersecurity, Anomaly Detection, CICIoT2023, Deep Learning, Data Imbalance},
        month = {April},
        }