  A new framework for a secure and robust consensus in blockchain-based IoT
networks is proposed using machine learning. Hyperledger fabric, which is a
blockchain platform developed as part of the Hyperledger project, though looks
very apt for IoT applications, has comparatively low tolerance for malicious
activities in an untrustworthy environment. To that end, we propose AI-enabled
blockchain (AIBC) with a 2-step consensus protocol that uses an outlier
detection algorithm for consensus in an IoT network implemented on hyperledger
fabric platform. The outlier-aware consensus protocol exploits a supervised
machine learning algorithm which detects anomaly activities via a learned
detector in the first step. Then, the data goes through the inherent Practical
Byzantine Fault Tolerance (PBFT) consensus protocol in the hyperledger fabric
for ledger update. We measure and report the performance of our framework with
respect to the various delay components. Results reveal that our implemented
AIBC network (2-step consensus protocol) improves hyperledger fabric
performance in terms of fault tolerance by marginally compromising the delay
performance.
