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@article{184106,
        author = {SK Sameer and Dr. Surender Kalyan},
        title = {OPTIMIZATION OF MACHINE LEARNING ALGORITHMS FOR INTRUSION DETECTION IN IOT NETWORKS USING RANDOM FOREST AND XGBOOST},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {208-218},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184106},
        abstract = {The high rate of growth of Internet of Things (IoT) devices has transformed modern infrastructure at the same time creating considerable security weaknesses caused by incapable computing resources as well as multiple architectures. Intrusion Detection Systems (IDS) is crucial in controlling malicious attacks over the IoT networks. In the proposed study, the application of machine learning algorithms namely Random Forest and XGBoost will be suggested, with the main idea to detect intrusions in the IoT setting successfully. Also work optimise these models by regularised methods of hyperparameter tuning and feature selection to obtain better detection performance and make them computationally efficient. This paper presents applying the models to benchmark datasets and assess the quality of those models in terms of several performance indicators such as accuracy, precision, recall, F1-score, and AUC-ROC. In the simulations, it can be seen that the optimized XGBoost model is effective with high detection accuracy and low execution time, compared to the Random Forest algorithm, which qualifies it as a good option of real-time intrusion detection within resource-constrained IoT network. Results help in coming up with powerful, scalable, and smart IDS customized to next-generation IoT ecosystems.},
        keywords = {Internet of Things (IoT), Intrusion Detection System (IDS), Machine Learning, Random Forest, XGBoost.},
        month = {September},
        }