Tolga O. Atalay (A2 Labs LLC), Tianyuan Yu (UCLA), Lixia Zhang (UCLA), Angelos Stavrou (A2 Labs LLC)

Cellular core networks are deployed as a set of Virtual Network Functions (VNFs) to dynamically provide customized connectivity for specific use cases. These VNFs are software-based applications whose trust management and security rely on well-established network domain solutions and certificate-based trust mechanisms. As VNFs are frequently redeployed, migrated, and scaled across a diverse ecosystem, the reliance on static trust solutions introduces bottlenecks and operational complexities. This approach to trust undermines the ability to ensure seamless, secure, and efficient interactions in a rapidly evolving cellular ecosystem. Addressing these challenges necessitates a fundamental shift toward an architectural foundation that inherently embeds security and trust into the communication fabric. Named Data Networking (NDN) offers such a foundation by focusing on data-centric security, where trust is embedded within the data itself rather than being dependent on external entities or channels. Leveraging named entities, NDN makes it possible to construct fine-grained trust relationships across cellular domains, tenants, and network slices. This paradigm shift enables the cellular core to move beyond static security solutions, providing a cohesive and scalable framework for managing trust in next-generation cellular networks. In this paper, we propose the adoption of the NDN network model to address the limitations of traditional approaches and achieve seamless security that evolves with the dynamic demands of 5G and beyond networks.

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VeriBin: Adaptive Verification of Patches at the Binary Level

Hongwei Wu (Purdue University), Jianliang Wu (Simon Fraser University), Ruoyu Wu (Purdue University), Ayushi Sharma (Purdue University), Aravind Machiry (Purdue University), Antonio Bianchi (Purdue University)

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Scale-MIA: A Scalable Model Inversion Attack against Secure Federated...

Shanghao Shi (Virginia Tech), Ning Wang (University of South Florida), Yang Xiao (University of Kentucky), Chaoyu Zhang (Virginia Tech), Yi Shi (Virginia Tech), Y. Thomas Hou (Virginia Polytechnic Institute and State University), Wenjing Lou (Virginia Polytechnic Institute and State University)

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Reinforcement Unlearning

Dayong Ye (University of Technology Sydney), Tianqing Zhu (City University of Macau), Congcong Zhu (City University of Macau), Derui Wang (CSIRO’s Data61), Kun Gao (University of Technology Sydney), Zewei Shi (CSIRO’s Data61), Sheng Shen (Torrens University Australia), Wanlei Zhou (City University of Macau), Minhui Xue (CSIRO's Data61)

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