Fifth generation (5G) and beyond cellular networks are still vulnerable to security threats, primarily due to the lack of integrity protection in the RRC layer. In order to address this problem, we propose a real-time anomaly detection framework that leverages the concept of distributed applications in 5G Open RAN networks.  Specifically, we identify PHY features that can generate a reliable fingerprint, infer in a novel way the time of arrival of uplink packets lacking integrity protection, and handle cross-layer features. By identifying legitimate message sources and detecting suspicious activities through an AI design, we demonstrate that Open RAN-based applications that run at the edge can be designed to provide additional security to the network. Our solution is first validated in extensive emulation environments achieving over 85\% accuracy in predicting potential attacks on unseen test scenarios. We then integrate our approach into a real-world prototype with a large channel emulator to assess its real-time performance and costs. Our solution meets the low-latency real-time constraints of 2ms, making it well-suited for real-world deployments.

 About Alessio Scalingi

Alessio Scalingi is a Ph.D. candidate of the Pervasive Wireless Systems Group at IMDEA Networks Institute since January 2020. He worked for six months as PhD intern at the Wireless Networks and Embedded Systems (WiNES) Laboratory in Boston (USA). His current main research interests include Collaborative Spectrum Sensing, Machine Learning, Spectrum Anomaly Detection, Open-RAN, Security in 5G & Beyond Networks. It is at the end of the PhD program in Telecommunication Engineering with the expected thesis defense in June 2024.

This event will be conducted in English