Data Science Methodology for Cybersecurity Projects
This work is incremental, as it reviews and compares established methodologies rather than introducing new ones, targeting cybersecurity practitioners seeking effective project frameworks.
The paper compares existing data science methodologies to address cybersecurity challenges, aiming to improve traditional static solutions by integrating data science for automated threat mitigation and awareness.
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.