A Survey of AI-Based Anomaly Detection in IoT and Sensor Networks

DOI Web Site 1 Citations
  • Kyle DeMedeiros
    Department of Computer Science and Statistics, College of Arts and Sciences, University of Rhode Island, 1 Upper College Road, Kingston, RI 02881, USA
  • Abdeltawab Hendawi
    Department of Computer Science and Statistics, College of Arts and Sciences, University of Rhode Island, 1 Upper College Road, Kingston, RI 02881, USA
  • Marco Alvarez
    Department of Computer Science and Statistics, College of Arts and Sciences, University of Rhode Island, 1 Upper College Road, Kingston, RI 02881, USA

Description

<jats:p>Machine learning (ML) and deep learning (DL), in particular, are common tools for anomaly detection (AD). With the rapid increase in the number of Internet-connected devices, the growing desire for Internet of Things (IoT) devices in the home, on our person, and in our vehicles, and the transition to smart infrastructure and the Industrial IoT (IIoT), anomaly detection in these devices is critical. This paper is a survey of anomaly detection in sensor networks/the IoT. This paper defines what an anomaly is and surveys multiple sources based on those definitions. The goal of this survey was to highlight how anomaly detection is being performed on the Internet of Things and sensor networks, identify anomaly detection approaches, and outlines gaps in the research in this domain.</jats:p>

Journal

  • Sensors

    Sensors 23 (3), 1352-, 2023-01-25

    MDPI AG

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