TIM-8315: v1

Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020). Cybersecurity data science: an overview from machine learning perspective. Journal of Big Data, 7 (1).
This journal article discusses the ultimate goal of cybersecurity data science as a data-driven intelligent decision-making process using security data for smart cybersecurity solutions. Implications for defense strategies are explored.

Riesco, R., & Villagrá, V. A. (2019). Leveraging cyber threat intelligence for a dynamic risk framework: Automation by using a semantic reasoner and a new combination of standards (STIX™, SWRL and OWL). International Journal of Information Security, 18 (6), 715–739.
This journal article discusses the use of an automated approach to cybersecurity threat intelligence. Specifically, the use case selected is an international leading organization in cybersecurity to demonstrate new dynamic ways to support decision-making at all levels while being under attack.

Yener, B., & Gal, T. (2019). Cybersecurity in the Era of Data Science: Examining New Adversarial Models. IEEE Security & Privacy Magazine, 17(6), 46–53.
This article discusses the use of data science capabilities in the area of cybersecurity, specifically to address the new threat model of the smart adversary.

Tubis, A. A., Werbińska-Wojciechowska, S., Góralczyk, M., Wróblewski, A., & Ziętek, B. (2020). Cyber-Attacks Risk Analysis Method for Different Levels of Automation of Mining Processes in Mines Based on Fuzzy Theory Use. Sensors (14248220), 20(24), 7210.
Optional Resource: This journal article describes the rising automation level and development of the Industry 4.0 concept in the mining sector due to the increased risk of cyber-attacks. The fuzzy theory is implemented in risk parameter estimation for cyber-attack scenario occurrence and implications are discussed.