Abstract
Nowadays, healthcare systems face ever-increasing cyber security threats due to the sensitive nature of patient data and the proliferation of IoT-enabled medical devices. Traditional Intrusion Detection Systems (IDS) struggle to adapt to healthcare environments' dynamic and heterogeneous data. Deep learning techniques, with their ability to analyze complex and high-dimensional data, have emerged as a promising approach to enhancing IDS performance. This paper explores the application of deep learning methods for intrusion detection in healthcare, focusing on their advantages, challenges, and practical implementations. Topics include neural network architectures, feature engineering for medical and network data, and real-time intrusion detection in resource-constrained environments. The goal is to provide a roadmap for leveraging deep learning to ensure robust and scalable security for healthcare systems.