Document Type
Article
Publication Date
4-17-2024
Abstract
Patient data is a critical concern in U.S. healthcare, and this research delves into how anomaly detection (Machine Learning) contributes to safeguarding patient information. Many studies have highlighted the growing cyber threats to healthcare data. However, those studies have certain limitations when they are closely examined. Recognizing these gaps, this research aims to explore the effectiveness of anomaly detection in enhancing cybersecurity measures for patient data. The study employs advanced machine-learning techniques, preferably anomaly detection, to predict and prevent cyber-attacks. The research introduces methods that address the shortcomings of prior studies and offer a better understanding of the role of anomaly detection in ensuring the safety of patient data. The findings of the study reveal that anomaly detection significantly enhances the healthcare industry's ability to identify and mitigate cyber threats and provide better defense against potential security breaches. These results have helped in improving cyber security practices within the health sector in the U.S. This study underscores the significance of integrating anomaly detection into cybersecurity strategies, ultimately contributing to the efforts to protect patient data in health centers.
Recommended Citation
Reddy, Vundyala Sumanth, "Enhancing Patient Data Security: Utilizing Machine Learning for Cyber Threat Protection in the U.S. Healthcare" (2024). Posters. 44.
https://digitalcommons.pittstate.edu/posters_2024/44
