Sarcouncil Journal of Engineering and Computer Sciences

Abstract: With rapid digitization within the United States healthcare sector, the dangers of cybersecurity threats are continuously combated using advanced machine learning (ML) algorithms. Cybersecurity in the healthcare industry faces a daunting set of challenges as data breaches become increasingly more sophisticated and traditional security measures fail to adequately protect sensitive patient data and critical healthcare systems. ML-driven systems have emerged with significant advantages to protecting critical healthcare systems including real-time threat detection, anomaly identification and predictive analytics, enhancing the overall resilience of a healthcare digital infrastructure. Nonetheless, despite these benefits, challenges of data privacy, algorithm bias and deployment complexities arise and need to be addressed to ensure reliability and ethical applications of ML technologies. The research work discusses the role of advanced machine learning (ML) algorithms in the detection and prevention of cybersecurity threats in healthcare systems. Through an extensive analysis of existing literature, this research examines how machine learning techniques such as deep learning, anomaly detection, and reinforcement learning improve defense through threat intelligence, intrusion detection and response. The findings reveal that anomaly detection models and deep learning frameworks greatly outperform the traditional rule-based systems for the detection of threats and behavioral anomalies in healthcare networks. Additionally, adaptive cybersecurity models that learn from novel attack patterns are increasingly utilizing reinforcement learning to induce systems that respond to attacks more proactively. However, despite these advances, there are still persistent gaps in terms of model interpretability, ethical governance, and deployment in real-world settings faced with heterogeneity of hospitals, emphasizing the need of regulatory frameworks and human-in-the-loop approaches. The findings offer valuable insights on how the U. S. healthcare security posture can be strengthened to build resilience against cybersecurity threats, while contributing to knowledge of the role of artificial intelligence approaches to securing healthcare digital infrastructure.