Securing Manufacturing Using Blockchain

TL;DR

This paper presents a blockchain-based multi-source anomaly detection framework for industrial control systems, enhancing security by integrating distributed log management with deep learning analysis across multiple data sources.

Contribution

It introduces a novel two-stage framework combining blockchain log management with multi-source deep learning for comprehensive ICS anomaly detection.

Findings

Achieved 95% precision in anomaly detection

Validated framework on factory automation and SWAT datasets

Outperformed single-source machine learning methods

Abstract

Due to the rise of Industrial Control Systems (ICSs) cyber-attacks in the recent decade, various security frameworks have been designed for anomaly detection. While advanced ICS attacks use sequential phases to launch their final attacks, existing anomaly detection methods can only monitor a single source of data. Therefore, analysis of multiple security data can provide comprehensive and system-wide anomaly detection in industrial networks. In this paper, we propose an anomaly detection framework for ICSs that consists of two stages: i) blockchain-based log management where the logs of ICS devices are collected in a secure and distributed manner, and ii) multi-source anomaly detection where the blockchain logs are analysed using multi-source deep learning which in turn provides a system wide anomaly detection method. We validated our framework using two ICS datasets: a factory automation dataset and a Secure Water Treatment (SWAT) dataset. These datasets contain physical and network level normal and abnormal traffic. The performance of our new framework is compared with single-source machine learning methods. The precision of our framework is 95% which is comparable with single-source anomaly detectors.