Data Science Methodology for Cybersecurity Projects

TL;DR

This paper reviews data science methodologies and compares their effectiveness in addressing cybersecurity challenges, emphasizing the importance of tailored approaches for improved threat mitigation.

Contribution

It introduces and compares popular data science methodologies specifically for cybersecurity projects, highlighting their strengths and weaknesses.

Findings

Data science enhances cybersecurity through advanced analytics and machine learning.

Methodology selection impacts effectiveness in threat detection and mitigation.

Comparison reveals strengths and limitations of different approaches in cyber-security context.

Abstract

Cyber-security solutions are traditionally static and signature-based. The traditional solutions along with the use of analytic models, machine learning and big data could be improved by automatically trigger mitigation or provide relevant awareness to control or limit consequences of threats. This kind of intelligent solutions is covered in the context of Data Science for Cyber-security. Data Science provides a significant role in cyber-security by utilising the power of data (and big data), high-performance computing and data mining (and machine learning) to protect users against cyber-crimes. For this purpose, a successful data science project requires an effective methodology to cover all issues and provide adequate resources. In this paper, we are introducing popular data science methodologies and will compare them in accordance with cyber-security challenges. A comparison discussion has also delivered to explain methodologies strengths and weaknesses in case of cyber-security projects.