Securing Cyber Infrastructure of IoT-Based Networks using AI and ML

The Internet of Things (IoT) has emerged as one of the most advanced technologies, a subject of interest or field of study to researchers, and a sector with promising economic prospects. Numerous gadgets must be connected to one another and people in order for them to work. To handle its data interchange and processing, IoT needs a cloud computing environment. To achieve the automation process in many applications, artificial intelligence (AI) is required to evaluate the data stored in cloud infrastructure and make quick, accurate judgments. IoT paired with Machine Learning (ML) and Deep Learning (DL) models are used to increase the functionality of complicated applications. Cloud vulnerability and IoT device networking remain significant dangers, although AI is presently playing a significant role in enhancing traditional cybersecurity. Huge volumes of data are generated by the rapidly evolving IoT devices and networks in many different ways, which calls for careful authentication and security. One of the most efficacious methods for confronting cybersecurity breaches and encrypting data is artificial intelligence (AI). In this paper, we have investigated the effectiveness of Artificial Intelligence (AI), deep learning (DL) and machine learning (ML) techniques for IoT security. Additionally, this investigation sheds light on the AI roadmap for identifying risks based on attack types.