Hybrid Machine Learning Framework for Anomaly Detection in 5G Networks

The rapid adoption of 5G networks has transformed the communication landscape, offering unprecedented speed, capacity, and connectivity for diverse applications such as IoT, autonomous vehicles, and critical infrastructure. However, this evolution also introduces vulnerabilities that can compromise network performance, security, and reliability. Anomaly detection, the process of identifying irregular patterns or deviations in network traffic, has emerged as a critical mechanism to ensure the resilience of 5G networks. It enables proactive identification of issues such as latency spikes, packet losses, Denial of Service (DoS) attacks, and other disruptions that could significantly degrade network quality. This research focuses on collecting and analysing 5G network traffic to detect and classify various anomalies. By leveraging advanced data collection techniques, we ensure comprehensive traffic coverage from diverse scenarios, including simulated and real-world environments. A systematic approach is used to reprocess the data, extract pertinent characteristics, then use cutting-edge machine learning techniques to identify anomalies.These models are tailored to address the particular difficulties presented by 5G networks, such as fast data flows, massive device connectivity, and dynamic network conditions. Key findings reveal the prevalence of specific anomalies such as throughput degradation, signalling storms, and malicious traffic patterns. The proposed framework achieves high accuracy in detecting these anomalies, demonstrating its potential for enhancing network reliability and security. Moreover, the findings underline the importance of integrating anomaly detection systems into 5G network management for real-time monitoring and automated mitigation.This work highlights the significance of anomaly detection in sustaining the performance and security of 5G networks. Future research could focus on real-time implementation and the integration of self-healing mechanisms to further enhance network robustness. Highlights Revolutionizing Connectivity: Explores the significance of anomaly detection in ensuring the reliability of high-speed 5G networks. Comprehensive Anomalies Dataset: Collection and analysis of diverse 5G network anomalies, including latency spikes, packet loss, and signalling storms. Dynamic Network Monitoring: Addresses challenges posed by the dynamic and high-speed nature of 5G data flows. Feature Extraction: Highlights the role of advanced preprocessing techniques to identify patterns and detect anomalies effectively. Security Enhancement: Detects cyber threats like Denial of Service (DoS) attacks and malicious traffic patterns in 5G networks. Scalability: Designed to accommodate the massive device connectivity and data volumes characteristics of 5G systems. Network Reliability: Proposes a proactive approach to mitigating network performance degradation caused by anomalies. Precision Detection: Achieves high detection accuracy using metrics such as precision, recall, and F1 scores. Signalling Storm Analysis: Identifies and mitigates issues caused by excessive signalling requests, a unique challenge in 5G. Automated Mitigation: Suggests integration of detection systems with automated mitigation mechanisms for faster response times. Impactful Insights: Discusses key findings on the impact of anomalies on network performance and security. Future-Proof Design: Advocates for integrating self-healing mechanisms and real-time anomaly detection into 5G network management systems. Pioneering Framework: Establishes a foundational approach for researchers and practitioners in anomaly detection for next generation networks.