Blockchain and AI: Securing Intelligent Networks for the Future

• URL: http://arxiv.org/abs/2604.06323v2
• Date: Thu, 09 Apr 2026 08:31:16 GMT
• Title: Blockchain and AI: Securing Intelligent Networks for the Future
• Abstract summary: This paper synthesizes the area through three reusable contributions.<n>The paper is intended as a reference for researchers and practitioners designing secure, transparent, and resilient intelligent networks.
• Score: 0.9239779358333178
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Blockchain and artificial intelligence (AI) are increasingly proposed together for securing intelligent networks, but the literature remains fragmented across ledger design, AI-driven detection, cyber-physical applications, and emerging agentic workflows. This paper synthesizes the area through three reusable contributions: (i) a taxonomy of blockchain-AI security for intelligent networks, (ii) integration patterns for verifiable and adaptive security workflows, and (iii) the Blockchain-AI Security Evaluation Blueprint (BASE), a reporting checklist spanning AI quality, ledger behavior, end-to-end service levels, privacy, energy, and reproducibility. The paper also maps the evidence landscape across IoT, critical infrastructure, smart grids, transportation, and healthcare, showing that the conceptual fit is strong but real-world evidence remains uneven and often prototype-heavy. The synthesis clarifies where blockchain contributes provenance, trust, and auditability, where AI contributes detection, adaptation, and orchestration, and where future work should focus on interoperable interfaces, privacy-preserving analytics, bounded agentic automation, and open cross-domain benchmarks. The paper is intended as a reference for researchers and practitioners designing secure, transparent, and resilient intelligent networks.

Related papers

• Toward Web 4.0: Bidirectional Trust between AI Agents and Blockchain [12.755719715389167]
  This paper presents a Systematization Knowledge organized around a bidirectional trust framework.<n>In the B direction, we examine how blockchain provides trust infrastructure for agents, spanning identity and account abstraction, permission and delegation, intent-centric execution, and tokenized agent economies.<n>In the A direction, we examine how AI agents participate in core blockchain mechanisms including security auditing, consensus, and governance.
  arXiv  Detail & Related papers  (2026-05-09T12:42:07Z)
• Autonomous Threat Detection and Response in Cloud Security: A Comprehensive Survey of AI-Driven Strategies [0.0]
  Cloud computing has changed online communities in three dimensions, which are scalability, adaptability and reduced overhead.<n>There are serious security concerns which are brought about by its distributed and multi-tenant characteristics.<n>The old methods of detecting and reacting to threats are becoming less and less effective even in the advanced stages of cyberattacks of cloud infrastructures.<n>The recent trend in the field of addressing these limitations is the creation of technologies of artificial intelligence (AI)
  arXiv  Detail & Related papers  (2026-01-06T04:19:27Z)
• Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm [85.7583231789615]
  6G positions intelligence as a native network capability, transforming the design of radio access networks (RANs)<n>Within this vision, Semantic-native communication and agentic intelligence are expected to play central roles.<n>Agentic intelligence endows distributed RAN entities with goal-driven autonomy, reasoning, planning, and multi-agent collaboration.
  arXiv  Detail & Related papers  (2025-12-04T03:09:33Z)
• SoK: Security and Privacy of AI Agents for Blockchain [4.706310663627593]
  Artificial Intelligence (AI)-based agents have emerged as valuable tools for interacting with blockchain environments.<n>While interest in applying AI to blockchain is growing, the literature still lacks a comprehensive survey that focuses specifically on the intersection with AI agents.<n>This paper addresses this gap by presenting the first Systematization of Knowledge dedicated to AI-driven systems for blockchain.
  arXiv  Detail & Related papers  (2025-09-08T18:32:15Z)
• A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks [49.90474228895655]
  Cloud-edge-terminal collaborative intelligence (CETCI) is a fundamental paradigm within the artificial intelligence of things (AIoT) community.<n>CETCI has made significant progress with emerging AIoT applications, moving beyond isolated layer optimization to deployable collaborative intelligence systems.<n>This survey describes foundational architectures, enabling technologies, and scenarios of CETCI paradigms, offering a tutorial-style review for CISAIOT beginners.
  arXiv  Detail & Related papers  (2025-08-26T08:38:01Z)
• Web3 x AI Agents: Landscape, Integrations, and Foundational Challenges [49.69200207497795]
  The convergence of Web3 technologies and AI agents represents a rapidly evolving frontier poised to reshape decentralized ecosystems.<n>This paper presents the first and most comprehensive analysis of the intersection between Web3 and AI agents, examining five critical dimensions: landscape, economics, governance, security, and trust mechanisms.
  arXiv  Detail & Related papers  (2025-08-04T15:44:58Z)
• Autonomous AI-based Cybersecurity Framework for Critical Infrastructure: Real-Time Threat Mitigation [1.4999444543328293]
  We propose a hybrid AI-driven cybersecurity framework to enhance real-time vulnerability detection, threat modelling, and automated remediation.<n>Our findings provide actionable insights to strengthen the security and resilience of critical infrastructure systems against emerging cyber threats.
  arXiv  Detail & Related papers  (2025-07-10T04:17:29Z)
• AI Flow: Perspectives, Scenarios, and Approaches [51.38621621775711]
  We introduce AI Flow, a framework that integrates cutting-edge IT and CT advancements.<n>First, device-edge-cloud framework serves as the foundation, which integrates end devices, edge servers, and cloud clusters.<n>Second, we introduce the concept of familial models, which refers to a series of different-sized models with aligned hidden features.<n>Third, connectivity- and interaction-based intelligence emergence is a novel paradigm of AI Flow.
  arXiv  Detail & Related papers  (2025-06-14T12:43:07Z)
• Ai-Driven Vulnerability Analysis in Smart Contracts: Trends, Challenges and Future Directions [0.2797210504706914]
  Vulnerabilities such as numerical overflows, reentrancy attacks, and improper access permissions have led to the loss of millions of dollars.<n>Traditional smart contract auditing techniques face limitations in scalability, automation, and adaptability to evolving development patterns.<n>This paper examines novel AI-driven techniques for vulnerability detection in smart contracts, focusing on machine learning, deep learning, graph neural networks, and transformer-based models.
  arXiv  Detail & Related papers  (2025-06-07T09:44:26Z)
• Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things [61.43014629640404]
  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.
  arXiv  Detail & Related papers  (2025-05-26T06:44:31Z)
• Internet of Agents: Fundamentals, Applications, and Challenges [68.9543153075464]
  We introduce the Internet of Agents (IoA) as a foundational framework that enables seamless interconnection, dynamic discovery, and collaborative orchestration among heterogeneous agents at scale.<n>We analyze the key operational enablers of IoA, including capability notification and discovery, adaptive communication protocols, dynamic task matching, consensus and conflict-resolution mechanisms, and incentive models.
  arXiv  Detail & Related papers  (2025-05-12T02:04:37Z)
• Blockchain As a Platform For Artificial Intelligence (AI) Transparency [0.0]
  "Black box" problem in AI decision-making limits stakeholders' ability to understand, trust, and verify outcomes.<n>This paper explores the integration of blockchain with AI to improve decision traceability, provenance data, and model accountability.<n>Findings suggest that blockchain could be a technology for ensuring AI systems remain accountable, ethical, and aligned with regulatory standards.
  arXiv  Detail & Related papers  (2025-03-07T01:57:26Z)
• Transforming Cyber Defense: Harnessing Agentic and Frontier AI for Proactive, Ethical Threat Intelligence [0.0]
  This manuscript explores how the convergence of agentic AI and Frontier AI is transforming cybersecurity.<n>We examine the roles of real time monitoring, automated incident response, and perpetual learning in forging a resilient, dynamic defense ecosystem.<n>Our vision is to harmonize technological innovation with unwavering ethical oversight, ensuring that future AI driven security solutions uphold core human values of fairness, transparency, and accountability while effectively countering emerging cyber threats.
  arXiv  Detail & Related papers  (2025-02-28T20:23:35Z)
• Considerations Influencing Offense-Defense Dynamics From Artificial Intelligence [0.0]
  AI can enhance defensive capabilities but also presents avenues for malicious exploitation and large-scale societal harm.<n>This paper proposes a taxonomy to map and examine the key factors that influence whether AI systems predominantly pose threats or offer protective benefits to society.
  arXiv  Detail & Related papers  (2024-12-05T10:05:53Z)
• AI-Protected Blockchain-based IoT environments: Harnessing the Future of Network Security and Privacy [0.0]
  This paper explores the pivotal role of artificial intelligence in bolstering blockchain-enabled IoT systems. When coupled with AI, these systems gain the ability to not only automate and optimize security protocols but also respond to new and evolving cyber threats. This paper aims to provide an in-depth understanding of how AI-enhanced blockchain technology can revolutionize network security and privacy in IoT environments.
  arXiv  Detail & Related papers  (2024-05-22T17:14:19Z)
• 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)
• Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Study [73.87110604150315]
  Generative Artificial Intelligence (GAI) has emerged as a promising solution to address challenges of blockchain technology. In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains.
  arXiv  Detail & Related papers  (2024-01-28T10:46:17Z)

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.