A Framework for IOC-Driven Early Warning Threat Intelligence - Raji, Adereti Rasak; O, Adenomon M.; Aimufua, Gilbert I. O.; Bassey, Steven I. (Kwaghe International Journal of Sciences and Technology, 2025)
The increasing sophistication of cyber threats necessitates a strategic transition from reactive defenses to proactive threat mitigation. Although Indicators of Compromise (IoCs) serve as essential forensic artifacts in post-incident analysis, their potential for early threat detection remains underutilized due to issues such as data overload, insufficient contextualization, and rapid obsolescence. This study proposes the IoC-Driven Early Warning (IDEW) framework to address these limitations. The IDEW framework introduces a structured, multi-stage approach that includes multi-source data aggregation, advanced IoC validation and scoring, real-time correlation and pattern detection, and the generation of context-rich early warnings. Through systematic processing, the framework enhances the accuracy and timeliness of threat detection, allowing organizations to identify and respond to emerging cyber threats at earlier stages. Grounded in current literature and operational insights, this framework offers a conceptual foundation for integrating IoCs more effectively into proactive cybersecurity strategies.