Decoding Ransomware: Crafting a Comprehensive Taxonomy and Actionable Insights for Risk Mitigation - Goyal, Swati; nshi, Himanshi (Journal of Advanced Research in Electronics Engineering and Technology; Vol 12 No 1&2 (2025): Journal of Advanced Research in Electronics Engineering and Technology, 2025)
This research addresses the critical gap in understanding and classifying ransomware by developing a comprehensive system that aligns with the evolving and complex landscape of ransomware threats. The research highlights the limitations of existing classification models, emphasising the need for a standardised framework that categorises ransomware based on its infection methods, propagation mechanisms, and sector-specific impact. By analysing extensive data from diverse ransomware families, this study identifies patterns and trends that inform the creation of a more effective classification system. The findings reveal that industries with high-value data, such as finance, manufacturing, critical infrastructure, and healthcare, face significant ransomware risks, leading to financial losses, operational disruptions, and regulatory challenges. This study provides organisations across sectors with critical insights to enhance their risk management and mitigation strategies. Beyond cybersecurity, the research underscores the need for stronger resilience measures, contributing to a broader discourse on digital security and operational continuity in an increasingly interconnected world. Ultimately, this classification framework serves as a valuable resource for policymakers, security professionals, and industry leaders, strengthening cybersecurity defences and response strategies against the growing ransomware threat.