SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach

TL;DR

This paper develops a SCADA testbed for cybersecurity research, utilizing machine learning algorithms to detect cyber-attacks in real-time, demonstrating effective detection performance and providing insights into attack impacts on SCADA systems.

Contribution

The paper introduces a realistic SCADA testbed integrated with machine learning-based attack detection, enabling practical cybersecurity research and evaluation in operational environments.

Findings

Machine learning models effectively detect cyber-attacks in real time.

The testbed simulates realistic attack scenarios for SCADA systems.

Performance comparison shows online deployment matches training results.

Abstract

This paper presents the development of a Supervisory Control and Data Acquisition (SCADA) system testbed used for cybersecurity research. The testbed consists of a water storage tank's control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naive Bayes and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environments