A Taxonomy for Cybercrime Incident Classification and Analysis

Abstract

This research proposes a systematic approach to describe and classify cybercrime incidents, addressing the current lack of standardized categorization. The methodology employs a multi-faceted approach combining existing frameworks with novel data analysis techniques to develop a comprehensive taxonomy. This taxonomy will improve incident response, threat intelligence sharing, and resource allocation by enabling more accurate analysis and prediction of cybercrime trends. The resulting taxonomy will be validated through a case study analysis of real-world cybercrime incidents. This research contributes to a more efficient and effective response to the ever-evolving landscape of cyber threats.

Introduction

Cybercrime is a pervasive and rapidly evolving threat, causing significant financial and reputational damage globally. Current methods for classifying cybercrime incidents are fragmented and inconsistent, hindering effective analysis, resource allocation, and international cooperation. The lack of a standardized taxonomy hampers the development of accurate predictive models and efficient incident response strategies. This research addresses this crucial gap by developing a comprehensive and systematic approach to describing and classifying cybercrime incidents, ultimately improving the effectiveness of cybersecurity efforts worldwide.

Objectives

• Develop a comprehensive taxonomy for classifying cybercrime incidents.
• Validate the taxonomy through a case study analysis of real-world incidents.
• Create a user-friendly tool for applying the taxonomy to new incidents.