Xingqi Wu (University of Michigan-Dearborn), Junaid Farooq (University of Michigan-Dearborn), Yuhui Wang (University of Michigan-Dearborn), Juntao Chen (Fordham University)

The decentralized and modular architecture of open radio access networks (O-RAN) enhances flexibility and interoperability but introduces significant challenges in efficiently managing resource allocation. The disaggregation of network functions across distributed unit, centralized unit, and RAN intelligent controller (RIC) creates complexities in coordinating resources across multiple network slices, each with distinct and dynamic quality of service (QoS) requirements. Traditional machine learning (ML) approaches for resource management often rely on extensive offline training, which is impractical in the highly variable and real-time environments of O-RAN systems. This paper presents LLM-xApp, a novel large language model (LLM)-powered xApp framework for adaptive radio resource management in O-RAN systems. The proposed framework is based on intelligently prompting LLM agents to dynamically optimize resource allocation to different network slices. Experimental evaluations are conducted on the OpenAI Cellular (OAIC) platform showcasing significant improvements in average data rates as well as the reliability of the slices, demonstrating the potential of LLMs to enhance real-time decision-making in next-generation wireless networks.

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EAGLEYE: Exposing Hidden Web Interfaces in IoT Devices via...

Hangtian Liu (Information Engineering University), Lei Zheng (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University), Shuitao Gan (Laboratory for Advanced Computing and Intelligence Engineering), Chao Zhang (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University), Zicong Gao (Information Engineering University), Hongqi Zhang (Henan Key Laboratory of Information Security), Yishun Zeng (Institute for Network Sciences…

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ABElity: Attribute Based Encryption for Securing RIC Communication in...

K Sowjanya (Indian Institute of Technology Delhi), Rahul Saini (Eindhoven University of Technology), Dhiman Saha (Indian Institute of Technology Bhilai), Kishor Joshi (Eindhoven University of Technology), Madhurima Das (Indian Institute of Technology Delhi)

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LADDER: Multi-Objective Backdoor Attack via Evolutionary Algorithm

Dazhuang Liu (Delft University of Technology), Yanqi Qiao (Delft University of Technology), Rui Wang (Delft University of Technology), Kaitai Liang (Delft University of Technology), Georgios Smaragdakis (Delft University of Technology)

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Automatic Library Fuzzing through API Relation Evolvement

Jiayi Lin (The University of Hong Kong), Qingyu Zhang (The University of Hong Kong), Junzhe Li (The University of Hong Kong), Chenxin Sun (The University of Hong Kong), Hao Zhou (The Hong Kong Polytechnic University), Changhua Luo (The University of Hong Kong), Chenxiong Qian (The University of Hong Kong)

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