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.

中文翻译：

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网络安全数据科学：从机器学习的角度概述

在计算环境中，最近几天，网络安全技术和运营发生了巨大变化，而数据科学则在推动这一变化。从网络安全数据中提取安全事件模式或见解并建立相应的数据驱动模型，是使安全系统自动化和智能化的关键。为了理解和分析带有数据的实际现象，使用了各种科学方法，机器学习技术，过程和系统，这通常称为数据科学。在本文中，我们重点讨论网络安全数据科学，并从相关的网络安全来源收集数据，而分析则补充了最新的数据驱动模式提供更有效的安全解决方案。与传统的网络安全领域相比，网络安全数据科学的概念使计算过程更具可操作性和智能性。然后，我们讨论并总结了许多相关的研究问题和未来的方向。此外，出于网络安全建模的目的，我们提供了一种基于机器学习的多层框架。总体而言，我们的目标不仅是讨论网络安全数据科学和相关方法，而且还将适用性集中在数据驱动的智能决策上，以保护系统免受网络攻击。