Abstract: This paper presents a hybrid framework that integrates intrusion detection and congestion prediction for 5G networks using supervised and unsupervised machine learning techniques. Unlike traditional approaches that focus only on congestion modeling, this work combines anomaly detection with congestion-aware forecasting to enhance both network reliability and security. The system is trained on the NSL-KDD dataset for identifying malicious traffic and on synthetic 5G congestion parameters for predicting potential bottlenecks. Supervised classifiers such as Random Forest and SVM are used to recognize labeled patterns, while clustering and anomaly detection methods capture emerging traffic behaviors without prior labels. The model is deployed through a Flask-based web platform that provides interactive dashboards, correlation heat-maps, attack category distributions, probability-based predictions, and real-time visualization of congestion risk. Additional features such as anomaly timelines, automated PDF reporting, and educational support pages make the system suitable for both cyber security learning environments and lightweight deployment in real networks. By combining predictive congestion control with intelligent intrusion detection, this framework offers a proactive, interpretable, and scalable solution for modern 5G communication infrastructures.

Key words: Network Anomaly Detection, Intrusion Detection System, Flask, Machine Learning, NSL-KDD

Downloads: | DOI: 10.17148/IARJSET.2025.12842