Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things

• URL: http://arxiv.org/abs/2505.23792v1
• Date: Mon, 26 May 2025 06:44:31 GMT
• Title: Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things
• Authors: Kai Li, Conggai Li, Xin Yuan, Shenghong Li, Sai Zou, Syed Sohail Ahmed, Wei Ni, Dusit Niyato, Abbas Jamalipour, Falko Dressler, Ozgur B. Akan,
• Abstract summary: Zero-Trust Foundation Models (ZTFMs) embed zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems.<n>ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments.
• Score: 61.43014629640404
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper focuses on Zero-Trust Foundation Models (ZTFMs), a novel paradigm that embeds zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems. By integrating core tenets, such as continuous verification, least privilege access (LPA), data confidentiality, and behavioral analytics into the design, training, and deployment of FMs, ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments. We present the first structured synthesis of ZTFMs, identifying their potential to transform conventional trust-based IoT architectures into resilient, self-defending ecosystems. Moreover, we propose a comprehensive technical framework, incorporating federated learning (FL), blockchain-based identity management, micro-segmentation, and trusted execution environments (TEEs) to support decentralized, verifiable intelligence at the network edge. In addition, we investigate emerging security threats unique to ZTFM-enabled systems and evaluate countermeasures, such as anomaly detection, adversarial training, and secure aggregation. Through this analysis, we highlight key open research challenges in terms of scalability, secure orchestration, interpretable threat attribution, and dynamic trust calibration. This survey lays a foundational roadmap for secure, intelligent, and trustworthy IoT infrastructures powered by FMs.

Related papers

• Endorsement-Driven Blockchain SSI Framework for Dynamic IoT Ecosystems [0.39462888523270856]
  Self-Sovereign Identity (SSI) offers significant potential for managing identities in the Internet of Things (IoT)<n>Existing SSI frameworks limit issuance credential and revocation to trusted entities, such as IoT manufacturers.<n>We propose a blockchain-based SSI framework that allows any individual with a verifiable trust linkage to act as a credential issuer.
  arXiv  Detail & Related papers  (2025-07-14T02:03:14Z)
• Federated Learning-Enhanced Blockchain Framework for Privacy-Preserving Intrusion Detection in Industrial IoT [0.0]
  Industrial Internet of Things (IIoT) systems have become integral to smart manufacturing, yet their growing connectivity has exposed them to significant cybersecurity threats.<n>Traditional intrusion detection systems (IDS) often rely on centralized architectures that raise concerns over data privacy, latency, and single points of failure.<n>We propose a novel Federated Learning-Enhanced Framework (FL-BCID) for privacy-preserving intrusion detection tailored for IIoT environments.
  arXiv  Detail & Related papers  (2025-05-21T11:11:44Z)
• On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective [334.48358909967845]
  Generative Foundation Models (GenFMs) have emerged as transformative tools.<n>Their widespread adoption raises critical concerns regarding trustworthiness across dimensions.<n>This paper presents a comprehensive framework to address these challenges through three key contributions.
  arXiv  Detail & Related papers  (2025-02-20T06:20:36Z)
• SoK: Unifying Cybersecurity and Cybersafety of Multimodal Foundation Models with an Information Theory Approach [58.93030774141753]
  Multimodal foundation models (MFMs) represent a significant advancement in artificial intelligence. This paper conceptualizes cybersafety and cybersecurity in the context of multimodal learning. We present a comprehensive Systematization of Knowledge (SoK) to unify these concepts in MFMs, identifying key threats.
  arXiv  Detail & Related papers  (2024-11-17T23:06:20Z)
• Robust Zero Trust Architecture: Joint Blockchain based Federated learning and Anomaly Detection based Framework [17.919501880326383]
  This paper introduces a robust zero-trust architecture (ZTA) tailored for the decentralized system that empowers efficient remote work and collaboration within IoT networks. Using blockchain-based federated learning principles, our proposed framework includes a robust aggregation mechanism designed to counteract malicious updates from compromised clients. The framework integrates anomaly detection and trust computation, ensuring secure and reliable device collaboration in a decentralized fashion.
  arXiv  Detail & Related papers  (2024-06-24T23:15:19Z)
• Securing Federated Learning with Control-Flow Attestation: A Novel Framework for Enhanced Integrity and Resilience against Adversarial Attacks [2.28438857884398]
  Federated Learning (FL) as a distributed machine learning paradigm has introduced new cybersecurity challenges. This study proposes an innovative security framework inspired by Control-Flow (CFA) mechanisms, traditionally used in cybersecurity. We authenticate and verify the integrity of model updates across the network, effectively mitigating risks associated with model poisoning and adversarial interference.
  arXiv  Detail & Related papers  (2024-03-15T04:03:34Z)
• Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
  Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
  arXiv  Detail & Related papers  (2024-02-21T06:22:41Z)
• The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
  Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge. This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI) We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
  arXiv  Detail & Related papers  (2024-01-03T07:47:22Z)
• Zero Trust: Applications, Challenges, and Opportunities [0.0]
  This survey comprehensively explores the theoretical foundations, practical implementations, applications, challenges, and future trends of Zero Trust. We highlight the relevance of Zero Trust in securing cloud environments, facilitating remote work, and protecting the Internet of Things (IoT) ecosystem. Integrating Zero Trust with emerging technologies like AI and machine learning augments its efficacy, promising a dynamic and responsive security landscape.
  arXiv  Detail & Related papers  (2023-09-07T09:23:13Z)
• A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy [47.89042524852868]
  Federated Learning (FL) stands out as a promising solution for diverse real-world scenarios. However, challenges around data isolation and privacy threaten the trustworthiness of FL systems.
  arXiv  Detail & Related papers  (2023-02-21T12:52:12Z)
• HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection [0.0]
  We propose a hierarchical blockchain-based federated learning framework to enable secure and privacy-preserved collaborative IoT intrusion detection. The proposed ML-based intrusion detection framework follows a hierarchical federated learning architecture to ensure the privacy of the learning process and organisational data. The outcome is a securely designed ML-based intrusion detection system capable of detecting a wide range of malicious activities while preserving data privacy.
  arXiv  Detail & Related papers  (2022-04-08T19:06:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.