Abstract
The Internet of Things (IoT) has revolutionised various sectors, including healthcare, education, agriculture, and military applications, by enabling seamless communication and data collection among interconnected devices. However, IoT networks' open and decentralised nature exposes them to many security threats and vulnerabilities. Intrusion Detection Systems (IDS) have been developed to address these challenges by identifying and mitigating malicious activities targeting these networks. Despite their importance, many organisations struggle to detect and prevent novel and sophisticated attacks effectively. This paper presents a comprehensive survey of the security issues inherent in IoT environments, emphasising the role of deep learning and machine learning techniques in enhancing IDS capabilities. By analysing existing vulnerabilities and evaluating various methodologies, we highlight the critical need for robust security measures that ensure IoT systems' reliability, privacy, and integrity. Through our findings, we advocate for integrating advanced analytical techniques in IDS to bolster defences against evolving threats in the IoT landscape.