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Handbook of Big Data Privacy pp 219–235Cite as

Anomaly Detection in Cyber-Physical Systems Using Machine Learning

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Abstract

Cyber-Physical Systems (CPS) are characterized by a wide range of complex multi-tasking components with close interaction that leads to integrating cyber sections into the physical world. Considering the significant growth of cyber-physical systems and due to the widespread use of smart features and communication tools, new challenges have emerged. In this regard, a new generation of CPSs such as the smart grid are facing different vulnerabilities and many threats and attacks. Therefore, the most important challenges for these systems are security and privacy. Anomaly detection is an important data analysis task as one of the approaches for CPSs security. As different anomaly detection methods are presented, it is difficult to compare the advantages and disadvantages of these techniques. Finally, in this chapter Machine Learning (ML) methods for detection of anomalies are presented through a case study which demonstrates the effectiveness of machine learning techniques at classifying False Data Injection (FDI) attacks.

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Author information

Authors and Affiliations

  1. School of Engineering, University of Guelph, Guelph, ON, Canada

    Hossein Mohammadi Rouzbahani, Hadis Karimipour & Abolfazl Rahimnejad

  2. Cyber Science Lab, School of Computer Science, University of Guelph, Guelph, ON, Canada

    Ali Dehghantanha

  3. Department of Mathematics and Computer Science, Brandon University, Brandon, MB, Canada

    Gautam Srivastava

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  1. Hossein Mohammadi Rouzbahani
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  2. Hadis Karimipour
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  3. Abolfazl Rahimnejad
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  4. Ali Dehghantanha
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  5. Gautam Srivastava
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Correspondence to Hossein Mohammadi Rouzbahani .

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Editors and Affiliations

  1. University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  2. Cyber Science Lab, School of Computer Science, University of Guelph, Guelph, ON, Canada

    Ali Dehghantanha

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Mohammadi Rouzbahani, H., Karimipour, H., Rahimnejad, A., Dehghantanha, A., Srivastava, G. (2020). Anomaly Detection in Cyber-Physical Systems Using Machine Learning. In: Choo, KK., Dehghantanha, A. (eds) Handbook of Big Data Privacy. Springer, Cham. https://doi.org/10.1007/978-3-030-38557-6_10

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  • DOI: https://doi.org/10.1007/978-3-030-38557-6_10

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