Machine Learning for Internet of Things (IoT) Security: A Comprehensive Survey

The Internet of Things (IoT) represents a network of interconnected gadgets, enabled by technology facilitating seamless communication between gadgets and the cloud. The adoption of IoT and its unique features expose these systems and devices to various intrusions. Traditional security methods are inadequate to secure IoT and requires to reevaluate the existing security protocols. While IoT devices come with built-in security features such as encryption and authentication, they require more advanced techniques to ensure robust system protection. Machine learning has emerged as a vital tool in enhancing IoT security, proving effective in mitigating cybersecurity risks and improving the intelligence of security systems. This survey provides a comprehensive overview of IoT systems, with a focus on their security aspects, including features, architectures, protocols, and associated risks. It also highlights recent algorithmic advancements, emphasizing the pivotal role of ML in strengthening IoT security. Furthermore, it categorizes attacks on IoT systems, offering a systematic understanding of vulnerabilities, and identifies relevant datasets to support future research efforts.