Exploiting and Securing ML Solutions in Near-RT RIC: A Perspective of an xApp
<|reference_start|>Exploiting and Securing ML Solutions in Near-RT RIC: A Perspective of an xApp: 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.<|reference_end|>
@article{dayaratne2024exploiting,
    title={Exploiting and Securing ML Solutions in Near-RT RIC: A Perspective of an
  xApp},
    author={Thusitha Dayaratne, Viet Vo, Shangqi Lai, Sharif Abuadbba, Blake
  Haydon, Hajime Suzuki, Xingliang Yuan, Carsten Rudolph},
    journal={arXiv preprint arXiv:2406.12299},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12299},
    primaryClass={cs.CR cs.NI cs.SY eess.SY}
}