Intrusion Detection in Cloud‐IoT Systems

Intrusion detection in Cloud-IoT systems is essential for safeguarding data, ensuring regulatory compliance, maintaining operational continuity, and protecting against the evolving landscape of cyber threats. Cloud-IoT systems collect huge amounts of data from diverse sources, including human-to-human, human-to-thing, and thing-to-thing interactions. These systems leverage cloud computing to process and study the data, delivering real-time insights seamlessly. The data in such systems become very vulnerable to intrusions as they can be compromised at the client's end, where the exchange of data primarily takes place. The data can also be prone to security issues in the cloud environment where the cloud servers can be exposed to attacks. Apart from firewalls that are positioned intelligently, there are needs for Intrusion Detection Systems (IDS). Intrusion detection in Cloud-IoT systems is a multifaceted but essential job to ensure the security and integrity of these environments. By exploiting leading-edge technologies like machine learning, deep learning, edge computing, and collaborative approaches, it is possible to develop robust IDS solutions that can successfully detect and alleviate threats in these dynamic and heterogeneous systems. Intrusion detection in Cloud-IoT systems is a fast-growing domain, with various research avenues developing to tackle the distinct challenges arising from the integration with the internet of things (IoT). This chapter offers a detail review of different strategies, tools and technologies for intrusion detection as well as explores future research directions.