Identifying DDoS Attack using Split-Machine Learning System in 5G and Beyond Networks

We rely on the internet heavily for our business in this day and age. As a result, fully connected or wireless internet access is essential. 5G networks involve different stakeholders and bring several challenges because of different security requirements and measures. Deficiencies in security management between these stakeholders can lead to security attacks. One of the most well-known and important cyber-attacks today is distributed denial-of-service (DDoS). This paper focuses on identifying DDoS attacks that block network availability by flooding the victim with a large amount of unlawful traffic, saturating its capacity, and preventing legitimate data from passing through. The rise of AI in recent years has given a machine learning model enhanced DDoS detection. AI helps in improving cybersecurity posture and at the same time cybersecurity can protect the compromise in AI and ML systems. In this paper, we present the hybrid threat detection mechanism using machine learning and human intelligence. The cyber threat detection mechanism is split between ML and Human intelligence (Split-ML). In this paper, we analyze DDoS attacks based on temporal and threshold behavior for various network communication protocols. Empirical results demonstrate the effectiveness of the proposed architecture and model in detecting and predicting DDoS attacks in fully connected or wireless networks.