Securing cloud data exchange related to IoT devices: key challenges and its machine learning solutions

The new trend of technology of the Internet of things (IoT), cloud computing, and smart economy is reaching the top of their adoption. The data created by these smart devices are continuously increasing pressure on the development of mass data handling techniques. The current need is managed by storing the data on cloud storage devices and becomes an integrated part of IoT data storage. This results in an increase in data loss, unauthorized data access, leakage of data, and private information loss that require adequate security measures. In this research work, a systematic review of potential security and privacy concerns of cloud data storage is discussed. Specifically, the architecture of the IoT infrastructure and its potential risks is followed by the security challenges. The machine learning approaches of automatic threat detection and management are summarized and suggested the tools as per the requirement of the user. The advantage of using ML tools for cloud data storage is better threat detection and notification, enhanced real-time response, better accuracy, and security compliance.