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1. Toward Secure and Efficient O-RAN Deployments: Secure Slicing xApp Use Case

   https://doi.org/10.1109/FNWF58287.2023.10520548  

   Moore, Joshua; Adhikari, Nisha; Abdalla, Aly S.; Marojevic, Vuk (November 2023, IEEE)

   The open radio access network (O-RAN) is recognized for its modularity and adaptability, facilitating swift responses to emerging applications and technological advancements. However, this architecture's disaggregated nature, coupled with support from various vendors, introduces new security challenges. This paper proposes an innovative approach to bolster the security of future O-RAN deployments by leveraging RAN slicing principles. Central to this security enhancement is the concept of secure slicing. We introduce SliceX, an xApp designed to safeguard RAN resources while ensuring strict throughput and latency requirements are met for legitimate users. Leveraging the open artificial intelligence cellular re-search (OAIC) platform, we observed that the network latency averages around ten microseconds in a default configuration without SliceX. The latency escalates to over seven seconds in the presence of a malicious user equipment (UE) flooding the net-work with requests. SliceX intervenes, restoring network latency to normal levels, with a maximum latency of approximately 2.3 s. These and other numerical findings presented in this paper affirm the tangible advantages of SliceX in mitigating security threats and ensuring that 0- RAN deployments meet stringent performance requirements. Our research demonstrates the real-world effectiveness of secure slicing, making SliceX a valuable tool for military, government, and critical infrastructure opera-tors reliant on public wireless communication networks to fulfill their security, resiliency, and performance objectives. 

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2. Actor-Critic Network for O-RAN Resource Allocation: xApp Design, Deployment, and Analysis

   https://doi.org/10.1109/GCWkshps56602.2022.10008713  

   Kouchaki, Mohammadreza; Marojevic, Vuk (December 2022, 2022 IEEE Globecom Workshops (GC Wkshps))

   Open Radio Access Network (O-RAN) has introduced an emerging RAN architecture that enables openness, intelligence, and automated control. The RAN Intelligent Controller (RIC) provides the platform to design and deploy network controllers. xApps are the applications that can leverage machine learning (ML) algorithms for near-real time control. Despite the opportunities provided by this new architecture, the progress of practical artificial intelligence (AI)-based solutions for network control and automation has been slow. There is a lack of end-to-end solutions for designing, deploying, and testing AI-based xApps in production-like network settings. This paper introduces an end-to-end O-RAN design and evaluation procedure using the latest O-RAN architecture and interface releases. We provide details on the development of a reinforcement learning (RL)-based xApp, considering two RL approaches and present numerical results to validate the xApp. 

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3. Universal Vertical Application Adaptation for O-RAN: Low-Latency RIC and Autonomous Intelligent xAPP Generation

   https://doi.org/10.1109/MCOM.001.2200907  

   Lien, Shao-Yu; Huang, Yi-Cheng; Tseng, Chih-Cheng; Lin, Shih-Chun; I, Chih-Lin; Xu, Xiaofei; Deng, Der-Jiunn (December 2023, IEEE Communications Magazine)
4. Integrated LLM-Based Intrusion Detection with Secure Slicing xApp for Securing O-RAN-Enabled Wireless Network Deployments

   Moore, Joshua; Abdalla, Aly Sabri; Khanal, Prabesh; Marojevic, Vuk (June 2025, IEEE ICC 2025 Eleventh Workshop on NextG (6G and beyond) Wireless Security - IEEE ICC 2025 Workshop - NextG-WiSec)
5. Integrated LLM-Based Intrusion Detection with Secure Slicing xApp for Securing O-RAN-Enabled Wireless Network Deployments

   https://doi.org/10.1109/ICCWorkshops67674.2025.11162166  

   Moore, Joshua; Abdalla, Aly Sabri; Khanal, Prabesh; Marojevic, Vuk (June 2025, IEEE International Conference on Communications workshops)

   The Open Radio Access Network (O-RAN) architecture is reshaping telecommunications by promoting openness, flexibility, and intelligent closed-loop optimization. By decoupling hardware and software and enabling multi-vendor deployments, O-RAN reduces costs, enhances performance, and allows rapid adaptation to new technologies. A key innovation is intelligent network slicing, which partitions networks into isolated slices tailored for specific use cases or quality of service requirements. The RAN Intelligent Controller further optimizes resource allocation, ensuring efficient utilization and improved service quality for user equipment (UEs). However, the modular and dynamic nature of O-RAN expands the threat surface, necessitating advanced security measures to maintain network integrity, confidentiality, and availability. Intrusion detection systems have become essential for identifying and mitigating attacks. This research explores using large language models (LLMs) to generate security recommendations based on the temporal traffic patterns of connected UEs. The paper introduces an LLM-driven intrusion detection framework and demonstrates its efficacy through experimental deployments, comparing non-fine-tuned and fine-tuned models for task-specific accuracy. 

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