Exploiting and Securing ML Solutions in Near-RT RIC: A Perspective of an xApp

TL;DR

This paper analyzes the security challenges of deploying machine learning applications on RAN Intelligent Controllers in O-RAN, highlighting vulnerabilities, potential attacks, and defense strategies to ensure robust 5G/6G network solutions.

Contribution

It provides a comprehensive analysis of security vulnerabilities and defense mechanisms for ML solutions in RIC platforms within O-RAN architecture.

Findings

Identification of key attack vectors on ML applications in RICs

Discussion of potential defense mechanisms for RIC security

Highlighting the need for standardized security protocols in O-RAN

Abstract

Open Radio Access Networks (O-RAN) are emerging as a disruptive technology, revolutionising traditional mobile network architecture and deployments in the current 5G and the upcoming 6G era. Disaggregation of network architecture, inherent support for AI/ML workflows, cloud-native principles, scalability, and interoperability make O-RAN attractive to network providers for beyond-5G and 6G deployments. Notably, the ability to deploy custom applications, including Machine Learning (ML) solutions as xApps or rApps on the RAN Intelligent Controllers (RICs), has immense potential for network function and resource optimisation. However, the openness, nascent standards, and distributed architecture of O-RAN and RICs introduce numerous vulnerabilities exploitable through multiple attack vectors, which have not yet been fully explored. To address this gap and ensure robust systems before large-scale deployments, this work analyses the security of ML-based applications deployed on the RIC platform. We focus on potential attacks, defence mechanisms, and pave the way for future research towards a more robust RIC platform.