Protecting and Mitigating Against Cyber Threats

27Enhancing Healthcare Security: A Revolutionary Methodology for Deep Learning-Based Intrusion Detection

M. Priyachitra^*, Prasanjit Singh, D. Senthil and Ellakkiya Sekar

Department of Computer Science and Engineering, St. Peter’s Engineering College (A), Hyderabad, Telangana, India

Abstract

The escalating influx of user information and network traffic in healthcare applications poses a significant challenge for effective network-intrusion-detection systems (NIDS). In this context, the need for robust Intrusion-Detection-Systems (IDS) becomes crucial, particularly in e-healthcare systems where patient medical details demand utmost confidentiality, precision, and high security. Any alterations to patient records can have profound implications on treatment outcomes, especially in critical cases like brain strokes. Existing studies predominantly focus on intrusion-based systems trained on outdated records and detection repositories, often leading to higher false positive rates. This calls for a paradigm change and a thorough strategy to deal with the constantly changing complexity of new threats. In this research, we provide a novel-hybrid-approach for intrusion detection in healthcare applications that combines the CNN network with the Gated Recurrent Unit (GRU) and the Ant Colony Optimization (ACO) algorithm. By improving the flexibility and precision of intrusion detection systems, this method seeks to provide reliable protection for private medical information in real-time ...