Bio: Chao Fang received his B.S degree in Information Engineering from Wuhan University of Technology, Wuhan, China, in 2009, and the Ph.D. degree with the State Key Laboratory of Networking and Switching Technology in Information and Communication Engineering from Beijing University of Posts and Te4lecommunications, Beijing, China, in 2015. He joined the Beijing University of Technology in 2016 and now is an associate professor. From August 2013 to August 2014, he had been funded by China Scholarship Council to visit Carleton University, Ottawa, ON, Canada, as a joint doctorate. Moreover, he is the visiting scholars of University of Technology Sydney, Commonwealth Scientific and Industrial Research Organization, Hong Kong Polytechnic University, Kyoto University, Muroran Institute of Technology, and Queen Mary University of London.

Dr. Fang is the senior member of IEEE, and the vice chair of technical affairs committee in IEEE ComSoc Asia/Pacific Region (2022-2023). Moreover, he served as the Technical Program Committee Chair of SPCNC 2024, Workshop Chairs of ICFEICT (2022-2024) and ICNCIC (2023-2024), and Poster Co-Chair of HotICN 2018. He won the Best Paper Award of IEEE ICFEICT 2022 and 2024, ICCSN 2024, and NCIC 2024. His current research interests include future networks, intelligent cloud-edge-terminal cooperation computing, and intelligent network control.

Abstract: In order to meet the service requirements of the emerging applications such as extended reality, 8K ultra-high definition video transmission and industrial Internet of Things in terms of massive user access, heterogeneous mobile traffic processing, ultra-low latency, ultra-high reliability and other aspects, cloud-edge collaboration, as the core of cloud radio access networks (C-RAN), has been increasingly concerned and risen to the height of national development strategy. At present, the problem on cooperative allocation and optimization of cloud-edge-end resources in C-RAN is still in the initial research stage, lacking systematic and in-depth research, which makes it difficult to adaptively guarantee the differentiated service requirements of network business. Therefore, by sorting out and referring to the research ideas and methods related to cloud computing and fog computing, and drawing on future network concepts such as "separation of control and forwarding" in software-defined networking and "in-network caching" in information-centric networking, the project focuses on collaborative allocation and intelligent optimization mechanisms of service-driven C-RAN resources from the perspective of cross-layer and cross-domain cooperation. To improve the overall service capacity and satisfy the differentiated service requirements of massive applications, key technologies such as multi-user-oriented cross-layer collaboration allocation and intelligent optimization of cloud-edge-terminal resources, multi-business-oriented cross-layer collaboration and intelligent resource allocation, multi-business-oriented cross-domain collaboration and intelligent resource allocation will be solved in C-RAN environments, providing customized service for network applications.

 

Gang Yang, Information Support Force Engineering University, China

Bio: Gang Yang is a cybersecurity researcher and a Committee Member of the Intelligent Evaluation Professional Committee at the Chinese Institute of Command and Control. He has served as a reviewer or PC member for prestigious conferences and journals, including TIP, ICASSP, and CogSci (all ranked CCF-A/B). His research portfolio includes leading 3 projects and contributing to 15 others, such as the National Key R&D Program of China. He has authored over 20 academic papers and holds 13 patents and software copyrights.

Abstrat: Modern information systems are evolving towards software-defined and intelligent paradigms, where the security of software systems themselves has become a critical safeguard for the entire information infrastructure. However, traditional software security testing typically require substantial manual efforts, making it challenging to keep pace with the expanding scale of software products. To this issue, we explore the application of Large Language Model(LLM) agent, and design workflows to uncover security vulnerabilities in third-party component libraries and Web interfaces. We map the human expert knowledge to specific testing phases within the agent's workflow and employing various control algorithms to control the testing actions to acquire an effective software testing. Experimental results demonstrate that our proposed solutions can effectively identify unknown vulnerabilities and, to a significant extent, substitute manual red teaming efforts, enabling more efficient and autonomous pentesting.