Signaling storming attack detection and mitigation in open radio access networks

Abstract

Open radio access networks (O-RAN) have revolutionized mobile communications by enhancing interoperability and fostering innovation through open interfaces and modular components. However, this flexibility and openness also introduce security vulnerabilities, particularly to signaling storm attacks that can disrupt network operations. Signaling storms occur when devices aggressively attempt to register or attach to the network at a high frequency, potentially orchestrated by an attacker to cause network disruptions in an O-RAN system: such attacks can be particularly detrimental due to the modular and open nature of the architecture. Currently, there is a lack of effective tools to swiftly detect and mitigate signaling storm attacks in O-RAN systems. This project aims to address this gap by developing a software microservice, i.e., xApp within O-RAN’s Near-real-time RAN Intelligent Controller that can detect and mitigate signaling storm attacks in near-real-time (in range of several milliseconds), thereby safeguarding the network's integrity and performance.. The proposed solution involves advanced detection algorithms and mitigation strategies implemented within an xApp framework, enabling real-time response to signaling storms. Key methodologies include the analysis of network traffic patterns and the development of machine learning models to identify abnormal behavior indicative of signaling storm attacks.