DistributedApps.ai

Publications

Explore the extensive collection of books and research papers by Ken Huang, a leading voice in AI, Web3, and cybersecurity.

Published Books

LLM Design Patterns

Packt, 2025

The Handbook for Chief AI Officers: Leading the AI Revolution in Business

Independently published, 2024

Agentic AI: Theories and Practices

Springer, 2025

AI Native LLM Security

Packt, 2024

Securing AI Agents: Foundations, Frameworks, and Real-World Applications

Springer, 2025

Generative AI Security: Theories and Practices

Springer, 2024

Web3: Blockchain, the New Economy, and the Self-Sovereign Internet

Cambridge University Press, 2024

Web3 Applications Security and New Security Landscape

Springer, 2024

Beyond AI: ChatGPT, Web3, and the Business Landscape of Tomorrow

Springer, 2023

Blockchain and Web3: Building the Cryptocurrency, Privacy, and Security Foundations of the Metaverse

Wiley, 2023

Practical Guide for AI Engineers

Self-published, 2024

A Comprehensive Guide for Web3 Security

Springer, 2023

Sample Published Research Papers

A novel zero-trust identity framework for agentic AI: Decentralized authentication and fine-grained access control

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)

Agent name service (ANS): A universal directory for secure AI agent discovery and interoperability

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)

Building a secure agentic AI application leveraging A2A protocol

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)

Securing GenAI multi-agent systems against tool squatting: A zero trust registry-based approach

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)

The trust fabric: Decentralized interoperability and economic coordination for the agentic web

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)

DIRF: A framework for digital identity protection and clone governance in agentic AI systems

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)

Towards unifying quantitative security benchmarking for multi agent systems

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)

Using the NANDA Index Architecture in practice: An enterprise perspective

This paper discusses the practical implementation and benefits of the NANDA Index Architecture from the perspective of enterprise-level applications.

arXiv:2508.03101 (2025)

ADA: Automated moving target defense for AI workloads via adaptive defense agents

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)

Agent capability negotiation and binding protocol (ACNBP)

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)

QSAF: A novel mitigation framework for cognitive degradation in agentic AI

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

Agent Name Service (ANS)
2nd Place Winner - UC Berkeley AgentX Competition

Our "Agent Name Service" (ANS) project provides a universal directory for secure AI agent discovery and interoperability.

IETF Proposals

View IETF Proposals

Other Related Research