Abstract: The number of Internet of Things (IoT) devices in smart homes is rapidly increasing, generating massive volumes of data that is mostly transmitted over wireless communication channels. The amount of data released by these gadgets increased as well. Aside from the increasing volume, the IoT gadget generates a significant amount of data. Having varied data quality characterised by its speed in terms of time and position in various different modalities dependence. However, different IoT

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... evices are vulnerable to different dangers, such as cyber-attacks and changing network conditions. Data leakage, connections, and so on. The properties of IoT nodes, on the other hand, make the current solutions obsolete. inadequate to cover the entire security range of IoT networks Machine learning can help in this situation. Algorithms can be useful in detecting irregularities in data, which improves the security of IoT systems. The methods are aimed at the data anomalies that exist in the smart Internet in general.