Using Different Methodologies of Data Science to Find Comparison Between Them for Cyber-Security
Keywords:
Cyber Security, CRISP-DM, KDD, FMDSAbstract
The main objective of this paper is that we have to find out which methodology is effective for Data Science for the cyber security problem. First of all, we discuss in the modern world, that data science is one form of topic where research spans many academic fields. It consists of scientific methods, procedures, formulas, and systems to gather information and work on that subject. When data sciences gather and store big data, analytical approaches can be used on cyber-security solutions. With the aid of a mathematical model, machine learning and big data analysis approaches can be used to manage the effects of threats. Huge amounts of data are the foundation of existing cyber-security solutions since more data allows for more accurate analysis. In data science, it is necessary to employ data analysis to resolve issues and provide answers to protect people from cybercrime projects. In this article, we compare the CRISP-DM, KDD process, and FMDS data science methodologies with their strong and weak points.
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