An Effective Data Analytics Approach to Cybercrime Underground Economy Using Ml Methodologies

Abstract

Despite the rapid escalation of digital threats, there has still been little research into the foundations of the subject or methodologies that could serve to manage Information Systems researchers and practitioners who deal with cyber security. In addition, little is referred to about Crime-as-a-Service (CaaS), a criminal plan of action that supports the cybercrime underground. This research gap and the practical cybercrime issues we face have motivated us to investigate the cybercrime underground economy by taking a data analytics approach from a design science point of view. To achieve this goal, we propose (1) a data analysis framework for analyzing the cybercrime underground, (2) CaaS and crime ware definitions, and (1) an associated classification demonstrate. In addition, we (1) build up an example application to demonstrate how the proposed framework and classification model could be actualized in practice. We at that point utilize this application to investigate the cybercrime underground economy by analyzing a large dataset obtained from the internet hacking community. By taking a design science research approach, this examination adds to the design of artifacts, foundations, and methodologies in this area. Additionally, it gives helpful practical bits of knowledge to practitioners by proposing rules as to how governments and organizations in all businesses can prepare for attacks by the cybercrime underground.