Development and Testing of a Vulnerability Identification Model That Uses Deep Learning Model in A Healthcare IoT Architecture

• Author(s): Chioma Juliet ; Ozioko Ekene Frank ; Ezugwu Lilian Martina
• Paper ID: 1709463
• Page: 10-20
• Published Date: 01-07-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 1 July-2025

Abstract

As a result of the speedy incorporation of Internet of Things (IoT) technologies in healthcare systems, it has managed to enhance service delivery, but also accelerated the appearance of potential cyber threats that jeopardize the safety of the patients, their data confidentiality, as well as the working dependability of the systems. This paper proposes the development and testing of a vulnerability identification model that uses deep learning model in a healthcare IoT setting. Three methods of deep neural networks (DNN) models, which comprise Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a combination of CNN+LSTM models, were trained and evaluated on real-life vulnerability data acquired at the University of Nigeria Teaching Hospital. Important performance vector was evaluated to test the models: loss, accuracy, precision, recall, and F1-score. The performance of CNN+LSTM hybrid model as evaluated revealed that it yielded the best result in all the metrics with an accuracy of 97 percent in training, 93 in validation, 94 percent precision, 95 percent recall and a f1-score of 94 percent. This would mean that there is better ability to identify and categorize vulnerabilities together with reduced false positives and anomalies. The conclusion points to the promise of hybrid deep learning methods in pursuing improved security in healthcare IoT systems in terms of real-time and reliable detection and response capabilities of threats.

Keywords

Healthcare; Internet of Things; Vulnerability; Deep Learning; CNN; LSTM

Citations

IRE Journals:
Chioma Juliet , Ozioko Ekene Frank , Ezugwu Lilian Martina "Development and Testing of a Vulnerability Identification Model That Uses Deep Learning Model in A Healthcare IoT Architecture" Iconic Research And Engineering Journals Volume 9 Issue 1 2025 Page 10-20

IEEE:
Chioma Juliet , Ozioko Ekene Frank , Ezugwu Lilian Martina "Development and Testing of a Vulnerability Identification Model That Uses Deep Learning Model in A Healthcare IoT Architecture" Iconic Research And Engineering Journals, 9(1)