Advancing Hospital Cybersecurity Through IoT-Enabled Neural Network for Human Behavior Analysis and Anomaly Detection

Abstract

The integration of Internet of Things (IoT) technologies in hospital environments has introduced transformative changes in patient care and operational efficiency. However, this increased connectivity also presents significant cybersecurity challenges, particularly concerning the protection of patient data and healthcare operations. This research explores the application of advanced machine learning models, specifically LSTM-CNN hybrid architectures, for anomaly detection and behavior analysis in hospital IoT ecosystems. Employing a mixed-methods approach, the study utilizes LSTM -CNN models, coupled with the Mobile Health Human Behavior Analysis dataset, to analyze human behavior in a cybersecurity context in the hospital. The model architecture, tailored for the dynamic nature of hospital IoT activities, features a layered. The training accuracy attains an impressive 99.53%, underscoring the model's proficiency in learning from the training data. On the testing set, the model exhibits robust generalization with an accuracy of 91.42%. This paper represents a significant advancement in the convergence of AI and healthcare cybersecurity. The model's efficacy and promising outcomes underscore its potential deployment in real-world hospital scenarios.

Topics & Concepts