Background: The rapidly expanding concept, the Internet of Things (IoT), connects a large number of devices to the internet for effective real-time data sharing and communication. But as IoT technology advances, new security risks also arise, and for this reason, machine learning (ML)-based intrusion detection systems (IDS), particularly anomaly-based ones, have emerged as a crucial defence against these new dangers to IoT networks.

Objective: The primary goal of this study is to develop and assess anomaly-based IDS based on ML techniques for use in Internet of Things environments. The goal is to improve the models' performance in terms of accuracy and usefulness.

Methods: The first phase of the study is a thorough review of the recent studies in the field to support the model design. The steps of the experimental stage include data preprocessing, encoding, and normalization. Data balancing was achieved using the SMOTEENN technique. The experiments and validation studies were performed on the UNSW-NB15 dataset. The employed ML algorithms for the study include DT, DNN, RF, XGBoost, and KNN, and the performance was in terms of precision, recall, accuracy, and F1-score.

Results: XGBoost recorded the best detection accuracy (96.37%), followed by RF and DNN. The models were evaluated on both normal and attack traffic, and the suggested model outperformed many existing approaches in recent literature.

Conclusion: The experimental findings confirm the suitability of hybrid and ensemble ML models in improving intrusion detection performance in IoT systems. Future research should consider integrating real-time datasets and combining deep learning with ensemble methods to develop more robust and adaptive models.

IoTs, IDS, Machine Learning, Anomaly Detection, SMOTEENN

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