Hybrid Machine Learning Techniques for Secure IoT Applications

Abstract

Web sensing devices capture and transmit data from the physical environment to a central place using rapid advances in software, hardware, and IoT technologies. Depending on the source, the overall count of web-connected devices is estimated to be between 50 and 100 billion by 2025. The amount of data released will increase as the population expands and technology improves, which is already happening. The Internet of Things (IoT) technology connects and interacts with the physical and virtual worlds. A gadget linked to the Internet is called an IoT. Intellectual data handling and investigation are required to construct smart IoT requests. This article gives knowledge about the Machine learning (ML) algorithms available for dealing with IoT data challenges, using smart cities as the primary use case. This article looks at common IoT diagnostic applications. This research compares and evaluates the predicted precision and understandability of supervised and unattended ML models. These technologies are briefly addressed in desktop, mobile, and cloud computing settings.