A New Deep Convolutional Neural Network Model for Multi-Class Cyber Attack Detection in Internet of Medical Things Networks

Abstract

The Internet of Medical Things (IoMT) has become an indispensable component of modern healthcare, with a wide range of applications from patient monitoring systems to smart medical devices. However, the limited processing power and memory capacity of IoMT devices make it difficult to implement traditional security mechanisms, leaving these devices vulnerable to cyber threats such as protocol attacks (e.g., DDoS, DoS, MQTT) and identity spoofing. Deep learning-based attack detection systems are gaining increasing importance in developing effective solutions against these attacks, which can directly threaten patient safety. In this study, DCNN—a new deep convolutional neural network-based model—is proposed to develop an effective attack detection system against the growing cyberattack threats in IoMT network environments. The model was evaluated on the CICIoMT2024 dataset, which consists of six classes: Benign, DDoS, DoS, MQTT, Reconnaissance, and Spoofing. The performance of the proposed DCNN was compared with re-implemented traditional machine learning models, including Random Forest, LightGBM, Decision Tree, Extra Trees, Logistic Regression, Naive Bayes, and Ridge Classifier, as well as state-of-the-art methods reported in the literature. The experimental results demonstrate that the proposed DCNN outperformed all comparison models, achieving 0.9999 or higher across all evaluation metrics, including accuracy, F1-score, precision, recall, and AUC.