Implementing and Evaluating an IoC-Driven Early Warning System for Enhanced Cybersecurity Resilience - Raji, Adereti Rasak; O., Adenomon M.; O., Gilbert I.; Bassey, Aimufua Steven I. (Darul Yasin Al Sys, 2025)
In the contemporary digital landscape, organizations are increasingly confronted by sophisticated cyber threats that render traditional reactive security measures inadequate, particularly in the face of advanced persistent threats (APTs) and rapidly evolving attack vectors. This paper proposes the design, implementation, and evaluation of an Indicator of Compromise (IoC)-driven Early Warning System (EWS) to proactively bolster cybersecurity resilience. Grounded in the principles of Cyber Threat Intelligence (CTI) and Design Science Research (DSR), the proposed framework termed the Intelligent Detection and Early Warning (IDEW) System integrates multiple threat intelligence feeds, employs advanced analytics for real-time threat detection, and delivers actionable insights to support timely incident response. The study explores the theoretical foundations of CTI and DSR, outlines key architectural considerations for the IDEW System, and presents a conceptual case study illustrating its application in identifying and mitigating emerging threats, including the 'Salt Typhoon' APT campaign. Additionally, the paper addresses challenges in operationalizing CTI, such as data integration, contextual relevance, and alert fatigue, and underscores the importance of human expertise, robust data governance, and iterative refinement for effective system deployment. This research contributes to the evolving discourse on proactive cybersecurity strategies, offering a structured, intelligence-driven approach to building adaptive and resilient defense mechanisms in a dynamic threat environment.