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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach

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Author(s):
Teixeira, Marcio Andrey [1, 2] ; Salman, Tara [1] ; Zolanvari, Maede [1] ; Jain, Raj [1] ; Meskin, Nader [3] ; Samaka, Mohammed [4]
Total Authors: 6
Affiliation:
[1] Washington Univ, Dept Comp Sci & Engn, St Louis, MO 63130 - USA
[2] Fed Inst Educ Sci & Technol Sao Paulo, Dept Informat, BR-15808305 Catanduva, SP - Brazil
[3] Qatar Univ, Dept Elect Engn, Doha 2713 - Qatar
[4] Qatar Univ, Dept Comp Sci & Engn, Doha 2713 - Qatar
Total Affiliations: 4
Document type: Journal article
Source: FUTURE INTERNET; v. 10, n. 8 AUG 2018.
Web of Science Citations: 5
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. (AU)

FAPESP's process: 17/01055-4 - Management platform for deployment and distribution of applications in multi-cloud environmen
Grantee:Marcio Andrey Teixeira
Support Opportunities: Scholarships abroad - Research