Exploring Machine Learning Applications for Enhancing Security and Privacy in Multimedia IoT
Suman Ruchika · 2024
During an earlier time, the advent of internet of things (IoT) tools boosted information generation. Many big data (BD), IoT, and analytics technologies have allowed users to derive valuable insights from massive volumes of multimedia data generated by IoT devices. The IoT includes a diverse set of linked machines. A great deal of data is transmitted between devices, between devices and business systems, and even between devices and individuals. Due to the billions of linked machines, device manipulation and data creation, server and network manipulation, identity and data theft, and the resulting influence on application platforms are all possibilities. One of the most critical technological development concerns nowadays is IoT security. Security encompasses device security, multimedia data transit and storage protection, and system and application security. Although there is a great deal of material on the subject, most of it does not offer a clear picture of the security and privacy issues that occur with IoT; this is a serious issue. The connection between BD analytics and the IoT is described. This chapter reviews the comparison of various machine learning (ML) classifiers presented based on the accuracy rate used to identify suitable classifiers for statistical data science challenges based on IoT security. In this chapter, there is a comparative analysis of previous studies based on various algorithms and an analysis of which algorithm is best.