Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Cybersecurity Data Science: An Overview from Machine Learning Perspective

Version 1 : Received: 9 June 2020 / Approved: 11 June 2020 / Online: 11 June 2020 (12:12:50 CEST)

A peer-reviewed article of this Preprint also exists.

Sarker, I.H., Kayes, A.S.M., Badsha, S. et al. Cybersecurity data science: an overview from machine learning perspective. J Big Data 7, 41 (2020). https://doi.org/10.1186/s40537-020-00318-5 Sarker, I.H., Kayes, A.S.M., Badsha, S. et al. Cybersecurity data science: an overview from machine learning perspective. J Big Data 7, 41 (2020). https://doi.org/10.1186/s40537-020-00318-5

Journal reference: Journal of Big Data 2020
DOI: 10.1186/s40537-020-00318-5

Abstract

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 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.

Subject Areas

Cybersecurity; machine learning; data science; decision making; cyber-attack; security modeling; intrusion detection; threat intelligence

Comments (3)

Comment 1
Received: 16 June 2020
Commenter: Borja Molina
Commenter's Conflict of Interests: I have another paper, previously published that follows a very similar argument and discussion, which is not referenced.
Comment: Awesome work! What a coincidence! It follows a very similar approach and discussion than a previous published work that, another purely coincidence, you have not referenced! https://arxiv.org/abs/2001.09697
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Comment 2
Received: 16 June 2020
Commenter: Borja Molina
The commenter has declared there is no conflict of interests.
Comment: Re-coocked article!
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Response 1 to Comment 2
Received: 17 June 2020
Commenter: Iqbal Sarker
The commenter has declared there is no conflict of interests.
Comment: Thanks for your comment. Your comment creates an opportunity for critical discussion regarding research in the area of cybersecurity. I like it. In the area of cybersecurity, there are several works including you relevant to Network Intrusion Detection, which is not our concern only. I have read your paper related to Network Intrusion Detection that you mentioned. Good one!! However, in this paper, we mainly focus on Cybersecurity Data Science and make a brief discussion from various kinds of machine learning like classification, prediction, regression, clustering, associations etc, as well as deep learning methods for various kinds of cybersecurity tasks. Thanks for understanding !!!

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