IoT Anomaly Detection Methods and Applications: A Survey

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. In recent publications, we observed a shortage of IoT anomaly detection methodologies, for example, when dealing with the integration of systems with various sensors, data and concept drifts, and data augmentation where there is a shortage of Ground Truth data. Finally, we discuss the present such challenges and offer new perspectives where further research is required.