Thaier Hamid

This paper presents a novel Intrusion Detection System (IDS) framework for securing Internet of Things (IoT) networks, leveraging advanced machine learning techniques. The proposed framework integrates Deep Neural Networks (DNNs) and Random Forest (RF) algorithms to enhance detection accuracy and robustness. Utilising the comprehensive CICIoT2023 dataset, the IDS model is rigorously trained and evaluated, demonstrating high efficacy in detecting and mitigating potential threats. However, the results also reveal shortcomings in detecting certain attack categories, such as command injection and SQL injection, indicating areas for further refinement. These findings contribute to the advancement of IoT security through the application of advanced machine learning techniques, while also highlighting the need for continued research to address identified shortcomings.