IoT Anomaly Detection Methods and Applications: A Survey
Ayan Chatterjee, Bestoun S. Ahmed

TL;DR
This survey reviews recent IoT anomaly detection methods and applications, highlighting current gaps and challenges such as data integration, concept drift, and lack of ground truth data, to guide future research directions.
Contribution
It provides a comprehensive categorization of IoT anomaly detection algorithms and identifies key gaps and challenges in recent research from 2019 to 2021.
Findings
Shortage of methodologies for sensor data integration
Challenges with data and concept drifts in IoT detection
Lack of ground truth data for effective anomaly detection
Abstract
Ongoing research on anomaly detection for the Internet of Things (IoT) is a rapidly expanding field. This growth necessitates an examination of application trends and current gaps. The vast majority of those publications are in areas such as network and infrastructure security, sensor monitoring, smart home, and smart city applications and are extending into even more sectors. Recent advancements in the field have increased the necessity to study the many IoT anomaly detection applications. This paper begins with a summary of the detection methods and applications, accompanied by a discussion of the categorization of IoT anomaly detection algorithms. We then discuss the current publications to identify distinct application domains, examining papers chosen based on our search criteria. The survey considers 64 papers among recent publications published between January 2019 and July 2021.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
