  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.
