Malware Detection in Industrial Scenarios Using Machine Learning and Deep Learning Techniques

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Author(s): Ángel Luis Perales Gómez (University of Murcia, Spain), Lorenzo Fernández Maimó (University of Murcia, Spain), Alberto Huertas Celdrán (University of Zurich, Switzerland)and Félix Jesús García Clemente (University of Murcia, Spain)
Copyright: 2022
Pages: 20
Source title: Advances in Malware and Data-Driven Network Security
Source Author(s)/Editor(s): Brij B. Gupta (National Institute of Technology, Kurukshetra, India)
DOI: 10.4018/978-1-7998-7789-9.ch005

Keywords: Cyber and Network Security / Information Science Reference / Information Security & Privacy / Security & Forensics

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Abstract

In the last decades, factories have suffered a significant change in automation, evolving from isolated towards interconnected systems. However, the adoption of open standards and the opening to the internet have caused an increment in the number of attacks. In addition, traditional intrusion detection systems relying on a signature database, where malware patterns are stored, are failing due to the high specialization of industrial cyberattacks. For this reason, the research community is moving towards the anomaly detection paradigm. This paradigm is showing great results when it is implemented using machine learning and deep learning techniques. This chapter surveys several incidents caused by cyberattacks targeting industrial scenarios. Next, to understand the current status of anomaly detection solutions, it analyses the current industrial datasets and anomaly detection systems in the industrial field. In addition, the chapter shows an example of malware attacking a manufacturing plant, resulting in a safety threat. Finally, cybersecurity and safety solutions are reviewed.

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Malware Detection in Industrial Scenarios Using Machine Learning and Deep Learning Techniques

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