A Survey of Machine Learning for IoT Networks

Abstract

In practice applications like Security Systems, intelligent infrastructure, traffic management and weather systems (among others), internet of things (IoT) is becoming more and more popular. Recently, the Internet of Things (IoT) has become more common because the number of intelligent devices used in everyday human life with minimal network life. The transfer of routing information plays a major role in providing communication between nodes. The IoT produces big data in addition to its increased volume, with a variety of multiple modalities and differing data quality, distinguished by its time and position dependence. Intelligent data processing and analysis are the main elements for the creation of intelligent IoT applications. This paper focuses on IoT network security aspects by exploring the serviceability of machine learning algorithms to identify anomalies in data from these networks. Taxonomy of machine learning algorithms is introduced to illustrate how various methods are applied to the data in order to obtain higher level knowledge. There will also be discussions on the ability and problems of Machine Learning for IoT data processing.