Cybersecurity data science: an overview from machine learning perspective

TLDR

Cybersecurity is rapidly evolving, and data science—particularly machine‑learning techniques—drives this shift by extracting incident patterns to automate and enhance security systems. The paper reviews cybersecurity data science, highlighting how data from relevant sources and analytics can inform intelligent decision‑making to protect systems from cyber‑attacks. The authors outline research challenges, future directions, and present a machine‑learning multi‑layered framework for cybersecurity modeling.

Abstract

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 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.