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

arXiv: https://arxiv.org/abs/2410.23306 · HTML: https://mlsanity.com/paper/2410.23306/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 26 references that are in the map.
- First posted on arXiv 26 Oct 2024.

## Built on by (most cited first, 3 of 3)

- [A Robust Cross-Domain IDS using BiGRU-LSTM-Attention for Medical and Industrial IoT Security](https://mlsanity.com/paper/2508.12470/) (2025)
- [A Hierarchical IDS for Zero-Day Attack Detection in Internet of Medical Things Networks](https://mlsanity.com/paper/2508.10346/) (2025)
- [An Improved CNN-LSTM Based Intrusion Detection System for IoT Networks](https://mlsanity.com/paper/2606.05776/) (2026)

## Abstract (arXiv)

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.