Abstract

Predicting cyber incidents based on data analytics for cybersecurity is a data-driven issue. Data used in prediction should be trustworthy, comprehensive, and typical without bias. This paper introduces data acquisition, data strategy, cyber incidents, and data analytics. Frameworks for supporting data sources, processing, cybersecurity, and related practices are presented. Data use cases and related technologies for cybersecurity in healthcare are studied. Health data are diverse and huge. Medical records are key data sources in healthcare. The roles and trends of cybersecurity for healthcare include data protection, secure communication, risk management, etc. Challenges in data-driven cyber incident prediction, customizing analytics models for various cyber incidents, and building resilient cyber systems are highlighted. The methods and related information in this paper help enhance cybersecurity in biomedical science and engineering, especially biomedical data engineering.