Enhancing 5G Network Security: A Deep Learning Framework for Real-Time DDoS Detection and Explainable Threat Analysis

With the rapid expansion of the Internet and the increasing reliance on digital services, cyberattacks have become more prevalent and sophisticated. This rise in malicious activities necessitates the development of stronger security measures, particularly across the physical, network, and application layers of the 5G architecture. Given the complexity and scale of modern networks, ensuring robust security mechanisms to mitigate cyber threats is crucial to maintaining service reliability and user trust. This paper proposes a DDoS detection framework capable of self-automating the detection and mitigation of denial-of-service (DoS) attacks as well as distributed denial-of-service (DDoS) attacks in next wireless generations. The framework utilizes machine learning (ML) and deep learning (DL) techniques to increase precision and optimize resource use regarding the detection of attacks on the 5G network’s application and transport layers. To test the efficacy of the suggested framework, a series of tests was conducted using diverse ML and DL models. The findings illustrate advanced cyber threat detection capabilities among some deep learning architectures. In binary classification of an attack, Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) indeed achieved the undisputable highest recall rate of 99.98%, confirming the systems’ effectiveness in discerning between normal and attack traffic. Also, while being involved in multi-attack classification, the Multi-Layer Perceptron (MLP) model surpassed other models in recall, reporting an impressive 99.04% rate. It can be observed that the suggested deep learning architectures have high effectiveness for enhancing 5G networks security related to ongoing DoS and DDoS threats. The findings revealed by the proposed framework do unlock a new perspective on enhancing network resilience against sophisticated cyber threats.