Using Different Methodologies of Data Science to Find Comparison Between Them for Cyber-Security

Sangeeta Devi; Pranjal Maurya; Munish Saran; Rajan Kumar Yadav; Upendra Nath Tripathi

doi:10.55524/ijirem.2022.9.5.7

Abstract

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.

Keywords

Cyber Security, CRISP-DM, KDD, FMDS.

References

Sarker IH et al (2020) Cyber security data science: an overview from machine learning perspective. J Big Data 7(1):1–29
https://www.comparitech.com/vpn/cybersecurity-cyber-crime-statistics-facts-trends/
Galeano-Brajones J et al (2020) Detection and mitigation of DoS and DDoS attacks in IoT-based stateful SDN: an experimental approach. Sensors 20(3):816
Meidan Y et al (2018) N-baiot—network-based detection of iot botnet attacks using deep autoencoders. IEEE Pervasive Comput 17(3):12–22
https://analyticsindiamag.com/top-10-datasets-for-cybersecurity-projects/
Shahzadi S et al. (2021) Machine learning empowered security management and quality of service provision in SDN-NFV environment
Yener B, Gal T (2019) Cyber security in the era of data science: examining new adversarial models. IEEE Secur Priv 17(6):46–53
Farhad Foroughi and Peter Luksch, ”Data Science Methodology for Cyber security project” https://www.researchgate.net> publication
Humayun M et al (2020) Cyber security threats and vulnerabilities: a systematic mapping study. Arab J Sci Eng 1–19
Wikipedia, “Cross Industry Standard Process for DataMining,” http://en.wikipedia.org/wiki/Cross Industry_Standard_Process, http://the-modeling-agency.com/crisp-dm.pdf
“Introduction To Hadoop –NYOUG”, https://nyoug.org>SIG>DataWarehousing
The DETER Project - Advancing the Science of Cyber Security Experimentation and Test. Terry Benzel, Jelena Mirkovic, et al. IEEE HST 2010 Conf, Boston, MA, November 2010.

Cites this article as

S. Devi, P. Maurya, M. Saran, R. K. Yadav, U. N. Tripathi, "Using Different Methodologies of Data Science to Find Comparison Between Them for Cyber-Security", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-9, Issue-5, Page No-53-58, 2022. Available from: https://doi.org/10.55524/ijirem.2022.9.5.7