Abstract
Anomaly detection in Internet of Things is a challenging issue and is being addressed in a wide range of domains, including fraudulent detection, mal- ware protection, information security and diagnosis of diseases. Due to the distributed nature of wireless transmission and the insufficient resources of end nodes, traditional anomaly detection techniques cannot be used in IoT directly. To extract uncommon behaviors or patterns from complex data, nevertheless, is a difficult task. As a result, this paper offers a thorough analysis of ML based methods to identify anomaly in the IoT healthcare data. Further, a detailed comparison of their performance is provided with reference to their benefits and disadvantages.
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