LLM-Powered Proactive Cyber-Defense Framework Using Cyber-Threat Indicators Collected from X Platform

Security organizations increasingly rely on cyber threat intelligence (CTI) sharing to enhance their resilience against cyberattacks. Indicators of Compromise (IoCs) play a critical operational role in CTI by providing malicious artifacts that support threat detection, incident response, and facilitate proactive defense. However, the rapid growth of social media as CTI sources, characterized by short-text content, poses significant challenges to automated IoC extraction, contextual interpretation, operational integration, and reliable verification. To address these challenges, this study proposes a comprehensive framework that integrates Large Language Models (LLMs) across multiple stages of the CTI pipeline. The framework leverages LLM-driven data augmentation, a hybrid classification model, and contextual summarization to enhance short-text understanding while supporting expert-in-the-loop validation for operational reliability. Extensive experimental evaluations demonstrate that LLM-driven data augmentation substantially improves model robustness and generalization while reducing false-positive alerts, achieving a precision of 98.87%. Quantitative diversity analysis and expert-based human evaluation further confirm the linguistic quality and correctness of the generated augmented samples. In addition, IoC reports are validated using both reference-based and reference-free evaluation metrics that show strong alignment and high semantic adequacy. Moreover, a technology acceptance model was integrated with cybersecurity domain constructs to assess the acceptance factors of the proposed framework. Regression analysis showed that perceived usefulness, behavioral intention, trust in automation, and risk were the strongest predictors of actual use. These predictors are commonly interpreted as indicators of technology acceptance.