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Encrypted DNP3 Traffic Classification Using Supervised Machine Learning Algorithms

Center of Mathematics, Computing and Cognition, Federal University of ABC, Campus São Bernardo do Campo, São Paulo 09606-070, Brazil
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Mach. Learn. Knowl. Extr. 2019, 1(1), 384-399; https://doi.org/10.3390/make1010022
Received: 24 November 2018 / Revised: 18 December 2018 / Accepted: 11 January 2019 / Published: 15 January 2019
The Distributed Network Protocol (DNP3) is predominately used by the electric utility industry and, consequently, in smart grids. The Peekaboo attack was created to compromise DNP3 traffic, in which a man-in-the-middle on a communication link can capture and drop selected encrypted DNP3 messages by using support vector machine learning algorithms. The communication networks of smart grids are a important part of their infrastructure, so it is of critical importance to keep this communication secure and reliable. The main contribution of this paper is to compare the use of machine learning techniques to classify messages of the same protocol exchanged in encrypted tunnels. The study considers four simulated cases of encrypted DNP3 traffic scenarios and four different supervised machine learning algorithms: Decision tree, nearest-neighbor, support vector machine, and naive Bayes. The results obtained show that it is possible to extend a Peekaboo attack over multiple substations, using a decision tree learning algorithm, and to gather significant information from a system that communicates using encrypted DNP3 traffic. View Full-Text
Keywords: smart grids; machine learning; DNP3; cyber security smart grids; machine learning; DNP3; cyber security
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de Toledo, T.R.; Torrisi, N.M. Encrypted DNP3 Traffic Classification Using Supervised Machine Learning Algorithms. Mach. Learn. Knowl. Extr. 2019, 1, 384-399.

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