ICBAC: an Intelligent Contract-Based Access Control framework for supply chain management by integrating blockchain and federated learning

2026-02-08

AI Summary

This study addresses the limitations of static, centralized access control mechanisms in cross-organizational supply chains, which struggle to mitigate insider threats and adapt to dynamic contextual changes. To overcome these challenges, the authors propose an intelligent contract-based access control framework that integrates permissioned blockchain with federated learning. Built upon Hyperledger Fabric’s multi-channel architecture, the framework employs three types of smart contracts to manage asset lifecycles and enable dynamic privilege revocation, while AI agents continuously monitor for anomalous behavior. Innovatively, it combines coalition game theory with a preference-stable coalition formation algorithm to facilitate efficient, privacy-preserving collaborative learning. Experimental results demonstrate that the system accurately detects anomalies in both IID and non-IID settings without exchanging raw data, achieving performance comparable to static approaches.

Technology Category

• Multiagent Systems: Mechanism Design
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

This paper addresses the critical challenge of access control in modern supply chains, which operate across multiple independent and competing organizations. Existing access control is static and centralized, unable to adapt to insider threats or evolving contexts. Blockchain improves decentralization but lacks behavioral intelligence, while centralized machine learning for anomaly detection requires aggregating sensitive data, violating privacy. The proposed solution is ICBAC, an intelligent contract-based access control framework. It integrates permissioned blockchain (Hyperledger Fabric) with federated learning (FL). Built on Fabric, ICBAC uses a multi-channel architecture and three smart contracts for asset management, baseline access control, and dynamic revocation. To counter insider misuse, each channel deploys an AI agent that monitors activity and dynamically restricts access for anomalies. Federated learning allows these agents to collaboratively improve detection models without sharing raw data. For heterogeneous, competitive environments, ICBAC introduces a game-theoretic client selection mechanism using hedonic coalition formation. This enables supply chains to form stable, strategy-proof FL coalitions via preference-based selection without disclosing sensitive criteria. Extensive experiments on a Fabric testbed with a real-world dataset show ICBAC achieves blockchain performance comparable to static frameworks and provides effective anomaly detection under IID and non-IID data with zero raw-data sharing. ICBAC thus offers a practical, scalable solution for dynamic, privacy-preserving access control in decentralized supply chains.

Problem

• access control
• supply chain management
• insider threats
• privacy preservation
• decentralization

Innovation

• Blockchain
• Federated Learning
• Access Control
• Smart Contracts
• Game Theory