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A new framework for a secure and robust consensus in blockchain-based IoTnetworks is proposed using machine learning. Hyperledger fabric, which is ablockchain platform developed as part of the Hyperledger project, though looksvery apt for IoT applications, has comparatively low tolerance for maliciousactivities in an untrustworthy environment. To that end, we propose AI-enabledblockchain (AIBC) with a 2-step consensus protocol that uses an outlierdetection algorithm for consensus in an IoT network implemented on hyperledgerfabric platform. The outlier-aware consensus protocol exploits a supervisedmachine learning algorithm which detects anomaly activities via a learneddetector in the first step. Then, the data goes through the inherent PracticalByzantine Fault Tolerance (PBFT) consensus protocol in the hyperledger fabricfor ledger update. We measure and report the performance of our framework withrespect to the various delay components. Results reveal that our implementedAIBC network (2-step consensus protocol) improves hyperledger fabricperformance in terms of fault tolerance by marginally compromising the delayperformance.

AI-enabled Blockchain: An Outlier-aware Consensus Protocol for Blockchain-based IoT Networks