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

## TL;DR

This paper introduces an AI-enabled blockchain framework with an outlier-aware consensus protocol that enhances fault tolerance in IoT networks using machine learning and Hyperledger Fabric.

## Contribution

It proposes a novel 2-step consensus protocol combining outlier detection with PBFT to improve fault tolerance in blockchain-based IoT networks.

## Key findings

- Improved fault tolerance in Hyperledger Fabric-based IoT networks.
- Marginal increase in delay performance due to the new protocol.
- Effective detection of anomaly activities using supervised machine learning.

## Abstract

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.