Abstract

Exploring creative methods to secure IoT networks is vital due to the enormous security concerns created by the rapid proliferation of the Internet of Things (IoT). To increase the security of the IoT, this study examines the use of artificial intelligence (AI), specifically deep learning (DL) as well as machine learning (ML) techniques. Three state-of-the-art DL algorithms—Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), Convolutional Neural Networks (CNN)—along with three ML methods—CatBoost, LightGBM, and XGBoost—are examined. These algorithms are renowned for their capability to handle big, as well as unbalanced datasets. This work test how well each algorithm can identify anomalies, categorize attacks, and forecast vulnerabilities using an IoT security dataset, such as CICIDS 2017 as well as IoT-23. The research evaluates algorithms by comparing their accuracy and training time. Classification tasks are where CatBoost and LightGBM really good, but when it comes to sequential data and complicated attack patterns, DL algorithms like CNN and LSTM are good. To provide the groundwork for creating AI-driven security solutions optimised for IoT systems, this research sheds light on the benefits and drawbacks of each method.