Farhad Foroughi and Peter Luksch, Institute of Computer Science University of Rostock, Germany

Cybersecurity 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 Cybersecurity. Data Science provides a significant role in cybersecurity by utilising the power of data (and big data), high-performance computing and data mining (and machine learning) to protect users against cybercrimes. 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 cybersecurity challenges. A comparison discussion has also delivered to explain methodologies’ strengths and weaknesses in case of cybersecurity projects.

Cybersecurity, Data Science Methodology, Data-Driven Decision-making, User Data Discovery, KDD Process, CRISP-DM, Foundational Methodology for Data Science, Team Data Science Process