Proposed Multicriteria Model For Community Detection Using Modified Cluster Walktrap On Social Media Network For Cybercrime

Abstract

The study begins by examining the unique characteristics of online social media platforms as fertile grounds for cybercriminal activities, including the dissemination of malware, phishing schemes, and illicit transactions. Drawing from the fields of computational intelligence, social network analysis, and cybersecurity, the research develops a framework for detecting and mapping out the intricate web of connections among cybercriminal actors within these platforms. Central to the proposed approach is the utilization of advanced data mining algorithms to extract valuable insights from vast amounts of social media data. By analyzing patterns of communication, user interactions, and behavioral attributes, computational intelligence tools can identify suspicious activities indicative of cybercriminal involvement. In conclusion, this study underscores the potential of computational intelligence tools in augmenting traditional cybersecurity approaches by harnessing the vast troves of data available on online social media platforms.