# Blockchain-Powered LSTM-Attention Hybrid Model for Device Situation Awareness and On-Chain Anomaly Detection

## TL;DR

This paper introduces a blockchain-powered LSTM-Attention model for detecting anomalies in industrial devices and ensuring secure data storage.

## Contribution

A novel hybrid model combining LSTM, attention mechanisms, and blockchain for secure and accurate anomaly detection in IIoT.

## Key findings

- The LEAD model achieves an F0.1 score of 0.96, outperforming RNN, LSTM, and Bi-LSTM models.
- Blockchain integration enables automated on-chain data verification and tamper alerts in a simulated FISCO-BCOS network.

## Abstract

With the increasing scale of industrial devices and the growing complexity of multi-source heterogeneous sensor data, traditional methods struggle to address challenges in fault detection, data security, and trustworthiness. Ensuring tamper-proof data storage and improving prediction accuracy for imbalanced anomaly detection for potential deployment in the Industrial Internet of Things (IIoT) remain critical issues. This study proposes a blockchain-powered Long Short-Term Memory Network (LSTM)–Attention hybrid model: an LSTM-based Encoder–Attention–Decoder (LEAD) for industrial device anomaly detection. The model utilizes an encoder–attention–decoder architecture for processing multivariate time series data generated by industrial sensors and smart contracts for automated on-chain data verification and tampering alerts. Experiments on real-world datasets demonstrate that the LEAD achieves an F0.1 score of 0.96, outperforming baseline models (Recurrent Neural Network (RNN): 0.90; LSTM: 0.94; and Bi-directional LSTM (Bi-LSTM, 0.94)). We simulate the system using a private FISCO-BCOS network with a multi-node setup to demonstrate contract execution, anomaly data upload, and tamper alert triggering. The blockchain system successfully detects unauthorized access and data tampering, offering a scalable solution for device monitoring.