AI-enabled Blockchain: An Outlier-aware Consensus Protocol for Blockchain-based IoT Networks
This addresses security and robustness issues in blockchain-based IoT networks, but it is incremental as it builds upon existing Hyperledger Fabric and PBFT protocols.
The authors tackled the problem of low tolerance for malicious activities in Hyperledger Fabric for IoT networks by proposing an AI-enabled blockchain with a 2-step consensus protocol that uses outlier detection. The result shows improved fault tolerance with a marginal compromise in delay performance.
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.