Comparative Study on Techniques Used for Anomaly Detection in IoT Data
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Abstract
The Internet of Things (IoT) makes it possible to connect various devices using wireless and cellular technology. As the foundation of Internet of Things (IoT), data from the target regions are gathered by widely dispersed sensing devices and delivered to the processing unit for aggregation and analysis. IoT service quality typically depends on reliability and integrity of data. However, IoT data gathered will be anomalous because of the unfavourable environment or equipment flaws. In order to ensure service quality, an efficient technique of anomaly detection is therefore essential. Finding new or unexpected things in the collected data is called Anomaly detection. The most important developments in recent years that enable automatic feature extraction from raw data are deep learning and machine learning. Role of machine and deep learning techniques to detect anomalies in sensor data is reviewed in this article. Finally, we provide a summary of the difficulties encountered currently in the anomaly detection field to identifying potential future research prospects.
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