Securing Healthcare with Deep Learning: A CNN-Based Model for medical IoT Threat Detection

TL;DR

This paper introduces a CNN-based model for detecting cyberattacks in IoMT healthcare systems, demonstrating superior accuracy over traditional methods and enhancing cybersecurity in medical IoT environments.

Contribution

The study presents a novel CNN approach tailored for IoMT threat detection, outperforming existing machine learning models on a comprehensive cybersecurity dataset.

Findings

Achieved 99% accuracy in threat detection

Outperformed traditional ML models like Logistic Regression and Random Forests

Validated on the CICIoMT2024 dataset with 18 attack types

Abstract

The increasing integration of the Internet of Medical Things (IoMT) into healthcare systems has significantly enhanced patient care but has also introduced critical cybersecurity challenges. This paper presents a novel approach based on Convolutional Neural Networks (CNNs) for detecting cyberattacks within IoMT environments. Unlike previous studies that predominantly utilized traditional machine learning (ML) models or simpler Deep Neural Networks (DNNs), the proposed model leverages the capabilities of CNNs to effectively analyze the temporal characteristics of network traffic data. Trained and evaluated on the CICIoMT2024 dataset, which comprises 18 distinct types of cyberattacks across a range of IoMT devices, the proposed CNN model demonstrates superior performance compared to previous state-of-the-art methods, achieving a perfect accuracy of 99% in binary, categorical, and multiclass classification tasks. This performance surpasses that of conventional ML models such as Logistic Regression, AdaBoost, DNNs, and Random Forests. These findings highlight the potential of CNNs to substantially improve IoMT cybersecurity, thereby ensuring the protection and integrity of connected healthcare systems.