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Cybersecurity data science: an overview from machine learning perspective
Ist Teil von
Journal of big data, 2020-07, Vol.7 (1), p.1-29, Article 41
Ort / Verlag
Cham: Springer International Publishing
Erscheinungsjahr
2020
Link zum Volltext
Quelle
Free E-Journal (出版社公開部分のみ)
Beschreibungen/Notizen
In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting
security incident patterns
or insights from cybersecurity data and building corresponding
data-driven model
, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss on
cybersecurity data science
, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the
latest data-driven patterns
for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated
research issues and future directions
. Furthermore, we provide a
machine learning
based
multi-layered framework
for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.