Blockchain systems are undergoing a fundamental transition from decentralized ledgers for digital assets to general-purpose trust infrastructures for verifiable computation, decentralized physical resources, and automated infrastructure management. Meanwhile, the limitations of the Blockchain as a Service (BaaS) model stem from a common structural problem: outsourcing control of infrastructure to third-party service providers inevitably involves a systemic surrender of trust, flexibility, and data sovereignty. RISC-V, with its open, modular, and extensible design, provides a general-purpose computing foundation for public blockchains that is open, low-level, compileable, verifiable, and scalable. Inspired by the development and characteristics of eSIM, the embedded Blockchain infrastructure management (eBIM) is defined as a software-hardware collaborative paradigm for blockchain infrastructure management with RISC-V. This study aims to provide a comprehensive survey on eBIM supporting research and technologies, to answer the following research questions (RQs): RQ1 What is eBIM? RQ2 How does eBIM work? RQ3 What can eBIM do? By introducing the concept of eBIM, this paper establishes a foundational reference for researchers, hardware architects, and protocol designers in this rapidly evolving landscape, including cryptographic acceleration, trusted execution environments, zero-knowledge virtual machines, and smart contract execution engines. The prospects of the proposed e-BIM and its future research directions are indicated in this paper.
Qinglin Yang, Yuan Liu, Yaoyao Zhang et al.· 0 citations
As new power systems become increasingly dependent on cloud-supported cyber-physical systems, their openness and interconnectivity continue to increase, thereby exposing risk points for advanced persistent threats (APTs). Deception defense has been widely regarded as an effective proactive approach for mitigating APT threats. However, the remarkable reasoning capabilities of large language models (LLMs) have enabled APT attackers to leverage LLM-based semantic understanding and task-planning capabilities to conduct automated, intelligent penetration attacks, while also bringing new challenges for traditional deception defense mechanisms. To address this issue, we propose a Chameleon service mechanism that constructs multiple types of LLM-oriented deceptive services based on the shared characteristics that LLMs exhibit during environment reconnaissance and target screening, and further incorporates an attack-defense game model with Minimax Q-learning for deployment. In this way, the proposed method increases the likelihood of trapping attackers while minimizing interference with normal power operations. The experimental results show that the proposed Chameleon service mechanism can effectively enhance the trapping effect of deceptive services on LLM-assisted attackers and demonstrate good effectiveness and stability across different candidate scales and LLM evaluation conditions. Our method can provide a feasible solution for proactive deception defense against intelligent attackers in new power systems.
Ying Yao, Yiji Lin, Qinglin Yang et al.· Fall Joint Computer Conferen...· 0 citations
A comprehensive survey of federated prompt learning (FPL) is presented to review recent advances in integrating the federated learning paradigm and large language models, while discussing security, privacy, and robustness issues and summarizing existing defense mechanisms.
Qinglin Yang, Chen Qiu, Hongyu Zhang et al.· 0 citations