Anomaly Detection in IoMT Using Hybrid Learning with Light Gradient Boosting

Registration ID: IJNRD_325513

Published ID: IJNRD2605696

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VANAPALLI LAKSHMI PAVANI, V. JEEVANKANTH, Dr. VSV DEEPAK

(IoMT), Anomaly Detection, Machine Learning, Cybersecurity

Real-time anomaly detection in Internet of Medical Things (IoMT) networks is critical due to increasing cyber threats targeting healthcare data and device communication. The original model utilized multiple traditional machine learning algorithms along with a stacking ensemble approach to detect anomalies using UNSW-NB15 and healthcare datasets. However, the use of conventional models limits performance when handling large-scale and complex data patterns. This work extends the existing framework by incorporating advanced boosting techniques, specifically Light Gradient Boosting Machine (LightGBM), to enhance detection capability. The system applies data preprocessing, feature engineering, and model training on both standalone and combined datasets to improve robustness. Experimental evaluation shows that the proposed LightGBM model achieves a maximum accuracy of 98.54%, outperforming all baseline and ensemble models, including the stacking meta-model. Additionally, the system is deployed using a Flask-based interface for real-time anomaly prediction. The proposed extension improves scalability, accuracy, and efficiency, making it suitable for secure IoMT-based healthcare environments.

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VANAPALLI LAKSHMI PAVANI, V. JEEVANKANTH, & Dr. VSV DEEPAK (May-2026). Anomaly Detection in IoMT Using Hybrid Learning with Light Gradient Boosting. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(5), g780-g785. https://ijnrd.org/papers/IJNRD2605696.pdf

Volume 11 Issue 5, May-2026

Pages : g780-g785

Paper Reg. ID: IJNRD_325513

Published Paper Id: IJNRD2605696

Research Area: Other area not in list

Author Type: Indian Author

Country: -, -, India

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