Anomaly Detection in Industrial Control Systems Based on Cross-Domain Representation Learning

This project is based on the research presented in the paper "Anomaly Detection in Industrial Control Systems Based on Cross-Domain Representation Learning", which has been accepted for publication in the IEEE Transactions on Dependable and Secure Computing (TDSC) in December 2024.

Overview

Industrial control systems (ICSs) are widely used in industry, and their security and stability are very important. Once the ICS is attacked, it may cause serious damage. Therefore, it is very important to detect anomalies in ICSs. ICS can monitor and manage physical devices remotely using communication networks. The existing anomaly detection approaches mainly focus on analyzing the security of network traffic or sensor data. However, the behaviors of different domains (e.g., network traffic and sensor physical status) of ICSs are correlated, so it is difficult to comprehensively identify anomalies by analyzing only a single domain. In this paper, an anomaly detection approach based on cross-domain representation learning in ICSs is proposed, which can learn the joint features of multi-domain behaviors and detect anomalies within different domains. After constructing across-domain graph that can represent the behaviors of multiple domains in ICSs, our approach can learn the joint features of them by leveraging graph neural networks. Since anomalies behave differently in different domains, we leverage a multi task learning approach to identify anomalies in different domains separately and perform joint training. The experimental results show that the performance of our approach is better than existing approaches for identifying anomalies in ICSs.

Installation

To install and run this project, follow these steps:

Prerequisites

• PyTorch 1.12.1 with CUDA 11.3
• PyTorch Geometric 2.1.0

Step 1: Clone the repository

git clone https://github.com/WenqiZhang-HIT/MGDN-project.git 
cd MGDN-project

Step 2: Install dependencies using install_packages.py

The project provides a script to install the required dependencies automatically. Run the following command to install the necessary libraries:

python install_packages.py

Step 3: Install your datasets

Place your dataset in the correct path under the ./data/your_datasets_name folder. For datasets requiring preprocessing, tools are provided in the ./util folder.

You can apply for the publicly available SWaT dataset by visiting the following link:

https://itrust.sutd.edu.sg/itrust-labs_datasets/dataset_info/

Step 4: Run the model

python main.py