Publications
Explore the extensive collection of books and research papers by Ken Huang, a leading voice in AI, Web3, and cybersecurity.
Published Books
Packt, 2025
Independently published, 2024
Springer, 2025
Packt, 2024
Springer, 2025
Springer, 2024
Cambridge University Press, 2024
Springer, 2024
Springer, 2023
Wiley, 2023
Self-published, 2024
Springer, 2023
Sample Published Research Papers
This paper introduces a new zero-trust framework for Agentic AI, focusing on decentralized identity and detailed access control to enhance security and autonomy in multi-agent systems.
arXiv:2505.19301 (2025)
This paper proposes the Agent Name Service (ANS), a universal directory designed to enable secure and seamless discovery and interaction between different AI agents.
arXiv:2505.10609 (2025)
This research outlines a methodology for developing secure Agentic AI applications by utilizing an Agent-to-Agent (A2A) protocol for protected communication and interaction.
arXiv:2504.16902 (2025)
This paper presents a zero-trust registry approach to defend multi-agent systems from 'tool squatting,' where malicious agents impersonate legitimate tools.
arXiv:2504.19951 (2025)
This paper explores the 'Trust Fabric,' a concept for decentralized coordination and economic interaction among AI agents on the emerging agentic web.
arXiv:2507.07901 (2025)
This paper introduces DIRF, a framework designed to protect the digital identities of AI agents and govern against unauthorized cloning and impersonation.
arXiv:2508.01997 (2025)
This work aims to create a standardized method for quantitatively measuring and comparing the security of different multi-agent AI systems.
arXiv:2507.21146 (2025)
This paper discusses the practical implementation and benefits of the NANDA Index Architecture from the perspective of enterprise-level applications.
arXiv:2508.03101 (2025)
This research introduces ADA, an automated defense system where adaptive AI agents protect other AI workloads using moving target defense strategies.
arXiv:2505.23805 (2025)
This paper details the Agent Capability Negotiation and Binding Protocol (ACNBP), a protocol for agents to securely negotiate and agree upon their capabilities and commitments.
arXiv:2506.13590 (2025)
This paper proposes QSAF, a new framework aimed at mitigating cognitive degradation in Agentic AI, ensuring long-term reliability and performance.
arXiv:2507.15330 (2025)
Award-Winning Research
Our "Agent Name Service" (ANS) project provides a universal directory for secure AI agent discovery and interoperability.