Assessing Machine Learning Techniques for Intrusion Detection in Cyber-Physical Systems

Vinícius F. Santos, Célio Albuquerque, Diego Passos, Silvio E. Quincozes and Daniel Mossé ()
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Vinícius F. Santos: Instituto de Computação, Universidade Federal Fluminense, Niteroi 24210-346, Brazil
Célio Albuquerque: Instituto de Computação, Universidade Federal Fluminense, Niteroi 24210-346, Brazil
Diego Passos: Instituto de Computação, Universidade Federal Fluminense, Niteroi 24210-346, Brazil
Silvio E. Quincozes: Campus Alegrete, Universidade Federal do Pampa, Bagé 96460-000, Brazil
Daniel Mossé: Computer Science Department, University of Pittsburgh, Pittsburgh, PA 15260, USA

Energies, 2023, vol. 16, issue 16, 1-18

Abstract: Cyber-physical systems (CPS) are vital to key infrastructures such as Smart Grids and water treatment, and are increasingly vulnerable to a broad spectrum of evolving attacks. Whereas traditional security mechanisms, such as encryption and firewalls, are often inadequate for CPS architectures, the implementation of Intrusion Detection Systems (IDS) tailored for CPS has become an essential strategy for securing them. In this context, it is worth noting the difference between traditional offline Machine Learning (ML) techniques and understanding how they perform under different IDS applications. To answer these questions, this article presents a novel comparison of five offline and three online ML algorithms for intrusion detection using seven CPS-specific datasets, revealing that offline ML is superior when attack signatures are present without time constraints, while online techniques offer a quicker response to new attacks. The findings provide a pathway for enhancing CPS security through a balanced and effective combination of ML techniques.

Keywords: cyber-physical systems; intrusion detection systems; offline machine learning; online machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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