With the rapid evolution of 5G mobile communication technology, cybersecurity faces new challenges, particularly in detecting sophisticated cyberattacks within these complex infrastructures. In this study, we introduce an innovative method that combines Federated Learning (FL) and Long Short-Term Memory (LSTM) networks to improve the detection of cyber threats targeting the GPRS Tunneling Protocol (GTP) in 5G networks.
Federated Learning empowers multiple devices to collaboratively analyze threats while preserving data privacy, offering a decentralized and efficient approach to security. The use of an unsupervised LSTM model enhances the system’s ability to detect anomalies without the need for extensive labeled datasets, providing a significant advantage for large-scale 5G implementations.
Our research focuses on identifying two critical cyber threats: Distributed Packet Forwarding Control Protocol (PFCP) attacks and IP address spoofing. These threats were emulated within a specially constructed 5G testing environment that mimics real-world conditions. The results demonstrate the effectiveness of our FL-LSTM model in detecting anomalies and safeguarding network traffic privacy.
Key contributions of the study include:
- A novel FL-LSTM-based approach to autonomously detect cyberattacks on 5G networks.
- Implementation of simulated attack scenarios to assess model performance in real-life conditions.
- Validation of the system’s effectiveness in improving the security and trustworthiness of 5G core infrastructures.
This research highlights the potential of Federated Learning to strengthen network security while addressing the growing complexity of future communication systems. As 5G continues to expand, ensuring robust cybersecurity through intelligent, automated solutions becomes a critical priority for both users and service providers.
Explore the full article to learn how our innovative approach could shape the future of cybersecurity in 5G and beyond.