Deep Learning Approach for Malware Classification and Threat Intelligence in Hospital Management
Main Article Content
Keywords
Deep learning, Malware classification, Threat intelligence, Hospital management systems, Healthcare cybersecurity, nternet of Medical Things (IoMT)
Abstract
The growing dependence on digital systems in hospital administration has increased exposure to malware attacks, thereby jeopardizing patient safety and data integrity. To improve healthcare cybersecurity, this paper suggests a deep learning approach for malware classification and integrated threat intelligence. For feature extraction, convolutional neural networks are used; for temporal behavior analysis, recurrent neural networks are applied; and an attention mechanism sorts high-risk threats. Superior detection accuracy, precision, and recall were attained with the framework using a hybrid dataset combining simulated malware samples with anonymized hospital system logs over those of traditional machine learning techniques. Moreover, a threat intelligence layer helps proactive defensive techniques by classifying malware families and tracking evolving attack vectors. The findings show that artificial intelligence can provide dependable, scalable, and adaptive protection for hospital information systems. The research offers both a methodological improvement in malware detection and a practical method of integrating threat intelligence into healthcare management, thereby ensuring continuity of clinical services and compliance with security requirements.
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