TIM-8315: v1

Alexander, C. A., & Wang, L. (2019). Cybersecurity, information assurance, and big data based on blockchain. 2019 SoutheastCon, SoutheastCon, 2019, 1–7.
This journal article details blockchain-based big data security and blockchain-based healthcare cybersecurity solution. Roles, applications, and challenges of blockchain for information security with big data are discussed.

Rassam, M. A., Maarof, M. A., & Zainal, A. (2017). Big data analytics adoption for cybersecurity: A review of current solutions, requirements, challenges and trends. Journal of Information Assurance & Security, 12 (4), 124–145.
This journal article details the need for big data analytics to be successfully adopted in the cyber threat intelligence and cyber-security landscape to deal with high data scales and sophisticated threats. The authors discuss the challenges that could result from such adoption and suggest some recommendations to overcome adoption challenges in future research.

Ullah, F., & Babar, M. A. (2019). Quantifying the impact of design strategies for big data cyber security analytics: An empirical investigation. 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2019 20th International Conference On, 146–153.
This journal article discusses how Big Data Cyber Security Analytics (BDCA) systems use big data technologies (e.g., Hadoop and Spark) for collecting, storing, and analyzing a large volume of security event data to detect cyber-attacks. The state-of-the-art uses various design strategies (e.g., feature selection and alert ranking) to help BDCA systems to achieve the desired levels of accuracy and response time.

Atapour-Abarghouei, A., McGough, A. S., & Wall, D. S. (2020). Resolving the cybersecurity data sharing paradox to scale up cybersecurity via a co-production approach towards data sharing. 2020 IEEE International Conference on Big Data (Big Data), Big Data (Big Data), 2020 IEEE International Conference On, 3867–3876.
Optional Resource: In this journal article, the authors suggest the practical solution of organizing co-productive data collection on a sectoral basis but acknowledge that common standards for data collection will also have to be developed and agreed upon. The authors propose an initial set of best practices for building collaborations and sharing data and argue that these best practices need to be developed and standardized to mitigate the paradox.