Integrating AI Models for Enhanced Real-Time Cybersecurity in Healthcare: A Multimodal Approach to Threat Detection and Response
Tariq Emad Ali, Faten Imad Ali, Farid Eyvazov, Alwahab Dhulfiqar Zoltán · Procedia Computer Science · 2025
As cyber threats become increasingly complex, the demand for advanced, real-time cybersecurity solutions in the healthcare sector has never been more critical. This research paper explores the integration of various artificial intelligence (AI) models and techniques to strengthen cybersecurity defenses in healthcare systems. By leveraging a multimodal approach that combines machine learning, deep learning, and anomaly detection algorithms, our method aims to significantly improve the speed and accuracy of detecting cyber threats in healthcare networks. The fusion of these diverse AI models enables the creation of a robust system capable of providing comprehensive protection against emerging threats, particularly those targeting sensitive healthcare data and infrastructure. Our approach ensures swift detection and response, safeguarding critical healthcare operations from potential cyberattacks and enhancing overall system security in real-time.