Novel Detection of Hospital Malware Using Network Pattern Analysis

Abstract

Advancements in machine learning offer potent solutions for cybersecurity, particularly in sensitive environments like hospital networks where the integrity and availability of data are paramount. In response to the escalating sophistication of cyber threats, a novel system utilizing adaptive machine learning algorithms was developed to detect malware with enhanced accuracy and responsiveness. This system demonstrated a notable capability to identify various types of malware, including zero-day attacks, achieving a detection accuracy of 92%. Comparative evaluations showed that this system significantly outperforms traditional signature-based systems and other contemporary machine learning-based approaches, which typically show lower accuracy and adaptability. The study revealed the importance of integrating real-time data analysis to adaptively refine detection algorithms, allowing the system to effectively respond to evolving threats, which not only enhances the security of hospital networks but also supports the broader application of intelligent, predictive cybersecurity measures in healthcare. Looking ahead, future work will focus on expanding the dataset to include more varied network conditions, integrating more diverse data sources, and employing federated learning techniques. These enhancements aim to improve scalability, enhance data privacy, and foster a collaborative security framework among healthcare institutions, further strengthening defenses against an ever-evolving landscape of cyber threats.