The concept of eBIM is introduced, which 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.
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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
