A State of the Art Review on the Integration of Blockchain and Machine Learning for DDoS Detection and Mitigation in 5G Networks

Abstract

With the rise of 5G networks, the surface for cyber attacks has expanded significantly, particularly in the form of Distributed Denial of Service (DDoS) attacks that threaten the availability of network services. While Machine Learning (ML) has proven effective in detecting such attacks, these models often face challenges related to trust, transparency, and centralized control. This paper proposes a hybrid approach that combines ML-based detection with Blockchain technology to enhance the security, traceability, and robustness of DDoS defense mechanisms in 5G environments. By utilizing Blockchain’s decentralized and tamper proof ledger, the system ensures that the outcomes of ML-based detections are securely recorded, verifiable, and resistant to manipulation. Smart contracts further enable automated and coordinated responses to threats across distributed network nodes. Crucially, the integration of ML and Blockchain enhances traceability, allowing detected malicious sources to be rapidly shared and acted upon across the network. This not only strengthens the reliability of detection but also significantly reduces the volume of DDoS traffic circulating in the network, by enabling earlier and more accurate blocking near the source. The proposed approach highlights how this synergy can improve both detection performance and overall network resilience