Comprehensive Review of IoT Attack Detection Using Machine Learning and Deep Learning Techniques

The cybersecurity landscape has been transformed by the Internet of Things (IoT), as it has connected countless objects to the Internet. However, this connectivity also brings about a significant cybersecurity risk. Malicious actors can exploit large IoT endpoints, potentially Which led to DDoS (Distributed Denial of Service) assaults. Traditional security methods, such as signature-based intrusion detection systems, have limitations, including human judgment and an inadequate ability to handle emerging threats like zero-day attacks. To address this challenge, Deep learning, and machine learning methods have become an effective solution. These techniques enable the identification of new threats and the adaptation to evolving attack strategies. This study offers a thorough analysis of the developments in deep learning and machine learning methods for identifying DDoS assaults in the IoT. This work will be a useful addition to the skills of researchers who are interested in IoT security.