Cyber Attack Detection in Healthcare Using Machine Learning and Multi Data Source
P Mithun, S. Janagiraman, K Deshra, Abinai sree P · 2024
Healthcare systems face a significant problem in detecting cyberattacks due to the increasing sophistication of attackers the framework uses sophisticated feature selection techniques and guarantees high accuracy reliability and security this paper presents a comprehensive framework for the detection and classification of attacks using machine learning algorithms like catboost and adaboost the framework leverages multi-source data from electronic health records medical device logs and network traffic by reaching an accuracy of 98% and a precision of 96% with adaboost the experimental findings show how successful the suggested method is additionally this research offers proactive identification and mitigation strategies that improve the cybersecurity resilience of healthcare systems