A Survey of AI-Based Anomaly Detection in IoT and Sensor Networks
👨‍🔬 Kyle DeMedeiros, Abdeltawab Hendawi, Marco Álvarez
📅 2023-01-25
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🔗 DOI: https://doi.org/10.3390/s23031352
📊 Citations: 123
🔓 Open Access

Abstract:
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.

References:
• An Energy-efficient And Trustworthy Unsupervised Anomaly Detection Framework (EATU) for IIoT
• An edge computing based anomaly detection method in IoT industrial sustainability
• Towards Semantic Management of On-Device Applications in Industrial IoT
• Rewiring with Positional Encodings for Graph Neural Networks
• Research on Time Series Anomaly Detection: Based on Deep Learning Methods
• Network Anomaly Detection Using Transfer Learning Based on Auto-Encoders Loss Normalization

#AI #IoT #Networking