Cybersecurity Threat Detection Using AI in 5G Networks

Abstract

The advent of 5G networks has brought about a significant transformation in telecommunications, enabling faster data speeds, lower latency, and a massive increase in device connectivity. The advent of 5G networks has brought about a significant transformation in telecommunications, enabling faster data speeds, lower latency, and a massive increase in device connectivity but it also increases the risk of cyber threats like DDoS attacks and ransomware.In India, a notable 52% rise in cyberattacks in 2021 has underscored the urgency for robust cybersecurity measures in 5G deployment. However, this expansion also introduces new security challenges, as the complexity and scale of the network make it more susceptible to cyber threats. Traditional cybersecurity measures may struggle to keep up with the dynamic nature of 5G environments, where real-time detection and response are crucial. This project focuses on leveraging Artificial Intelligence (AI) for threat detection in 5G networks. Specifically, we propose the use of machine learning (ML) and deep learning (DL) techniques to enhance the identification of anomalous behaviour, intrusions, and malicious activities within the 5G infrastructure. By analyzing network traffic patterns, user behaviour, and system logs, AI algorithms can detect emerging threats and adapt to evolving attack strategies. This approach aims to offer faster, more accurate, and scalable solutions compared to traditional methods. The project explores various AI-based models, including supervised and unsupervised learning, reinforcement learning, and anomaly detection techniques, to improve the resilience of 5G networks against cyberattacks. The implementation of AI-driven cybersecurity solutions will significantly bolster the security posture of 5G networks, ensuring the protection of critical data and infrastructure in an increasingly connected world.