Investigating the Application of Artificial Intelligence (AI) and Machine Learning (ML) Techniques to Enhance Cybersecurity for Internet of Things (IoT) Devices, Prevent Data Breaches, and Safeguard User Privacy

The rapid expansion of the Internet of Things (IoT) ecosystem has provided a tremendous attack surface, and therefore, IoT devices are highly vulnerable to advanced cyberattacks, data breaches, and privacy invasions.Rule-based intrusion detection systems are mostly ineffective in dealing with highdimensional and heterogeneous traffic streams that IoT environments produce.To fill in these gaps, this research examines the systematic use of Artificial Intelligence (AI) and Machine Learning (ML) methods towards IoT security augmentation, malicious activity detection, blocking of data leakage, and safeguarding of user privacy.A strict methodology, quantitative experimental approach was adopted, leveraging the Australian Centre for Cyber Security's TON_IoT20 dataset of actual network traffic, attack behaviours (i.e., DDoS, data injection, password-based intrusions), and normal run log data from various IoT devices such as smart plugs, cameras, and thermostats.Data preprocessing steps involved removal of duplicates, handling of missing values by imputation, feature encoding, and scaling, followed by a 70/15/15 stratified split for training, validation, and test.Three standard ML models, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and a Deep Neural Network (DNN) were used in Python under a controlled Ubuntu environment and trained on the pre-processed data.