AI Approaches for Detecting and Mitigating DDoS Attacks in 5G Networks: A Systematic Literature Review

The rollout of fifth generation (5G) networks is reshaping connectivity, enabling ultra-fast data transmission and low latency for innovative applications across various sectors. However, this transformation introduces significant security challenges, particularly the threat of Distributed Denial of Service (DDoS) attacks that exploit these capabilities. Despite the development of various security measures, effectively detecting and mitigating DDoS attacks remains a pressing challenge for researchers and practitioners alike. This systematic literature review (SLR) investigates machine learning (ML), deep learning (DL), and hybrid approaches for DDoS detection published between 2019 and 2024. Key aspects such as methodologies, datasets, preprocessing strategies, hyper-parameter optimization, and performance metrics are thoroughly analyzed. The findings reveal critical research gaps and emerging trends, providing valuable insights to enhance the resilience of 5G networks against evolving threats. Ultimately, this study establishes a solid foundation for future work aimed at safeguarding the transformative potential of 5G connectivity in an increasingly interconnected world.