Anomaly detection for industrial Internet of Things devices based on self-adaptive blockchain sharding and federated learning

Abstract

With the rapid growth of the Industrial Internet of Things (IIoT), more and more devices are connecting to the network, generating vast amounts of data, including sensors, actuators, and controllers. Traditional anomaly detection methods often rely on centralized data collection, leading to concerns about privacy leakage and data centralization. To address these challenges, approaches that combine federated learning (FL) with blockchain technology offer an efficient, scalable solution. These methods enable automatic scaling based on system size, adapting to increasing devices and data traffic. However, the limitations of fixed shards and security risks associated with the aggregation of data from different shards introduce new issues. To overcome these challenges, this paper proposes a self-adaptive blockchain sharding strategy, based on IIoT device grouping, which jointly optimizes the number of shards and the security of model updates. This optimization is modeled as a Markov Decision Process (MDP), with deep reinforcement learning (DRL) used to determine the optimal device sharding parameters. Furthermore, a joint committee mechanism is introduced to ensure secure cross-shard transactions, while a federated learning filtering mechanism (Fed-Filt) is applied to enhance the accuracy and security of global model aggregation by screening out malicious nodes. In the clustering experiments, the proposed method shows significant improvements in clustering quality metrics such as Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), Adjusted Mutual Information (AMI), and silhouette score, with faster convergence. Experimental results demonstrate that under 25% and 50% malicious node scenarios, the proposed algorithm can effectively resist poisoning attacks and achieve stable convergence, outperforming the traditional FedAvg algorithm. Specifically, with 50% malicious nodes, the accuracy is improved by approximately 27.8%, and the method exhibits strong resistance and recovery capabilities.