Anomaly Detection in IoT Device Traffic Data Using Deep Learning

Abstract

The Internet of Medical Things (IoMT) is important in modern healthcare systems by enabling continuous monitoring and data­driven medical services. However, adopting IoMT is associated with cybersecurity challenges due to the different protocols and vulnerabilities to network­based threats. This study examines anomaly detection in IoMT traffic utilizing the CICIoMT2024 dataset, a comprehensive benchmark that includes 18 cyberattacks across 40 devices in Wi­Fi, Bluetooth, and MQTT environments. We evaluate machine learning models such as Logistic Regression, Random Forest, AdaBoost, and Deep Neural Networks (DNN). Additionally, we explore deep learning architectures that combine convolutional layers with Long Short­Term Memory (LSTM) units. Our experiments cover a range of model assessments for binary, six­class, and nineteen­class classification tasks. Model performance is measured using standard metrics such as accuracy, precision, recall, and F1­score. This research demonstrates the potential of deep learning to enhance the security of IoMT networks and supports further development of better detection models in the healthcare environment.