AI‐Powered Anomaly Detection for Secure Internet of Things (IoT): Optimising XGBoost and Deep Learning With Bayesian Optimisation

ABSTRACT Intelligent and adaptive defence systems that can quickly thwart changing cyberthreats are becoming more and more necessary in the dynamic and data‐intensive Internet of things (IoT) environment. Using the NSL‐KDD benchmark dataset, this paper presents an improved anomaly detection system that combines an optimised sequential neural network (OSNN) with an XGBoost model optimised using Bayesian approaches. Key drawbacks of conventional intrusion detection systems (IDSs), including manual hyperparameter tuning, extensive preprocessing and ineffective optimisation techniques, are successfully addressed by the suggested solution. By automating the adjustment of key parameters, such as learning rate, tree depth and regularisation strength, Bayesian optimisation enhances model convergence, prediction accuracy and generalisation. Superior binary classification performance was attained by the optimised XGBoost, which had 99.98% accuracy, 99.94% F1 score and 99.95% MCC. In a similar vein, the Bayesian‐optimised OSNN obtained flawless precision and recall for the normal and U2R classes, achieving 99.92% overall accuracy in multiclass identification. Across all attack types, its CNN‐based architecture showed excellent sensitivity and specificity, attaining almost flawless AUC values. The suggested Bayesian‐optimised framework offers a highly accurate, scalable and self‐adaptive intrusion detection solution for actual IoT network environments, outperforming traditional machine and deep learning models by a large margin overall.