Hunting the Website Owner: How Large Language Models Are Revolutionizing Cybersecurity Attribution
A new framework called OwnerHunter leverages large language models (LLMs) to tackle the critical cybersecurity challenge of identifying the true owner or organization behind a website. This task is vital for phishing detection, incident response, and threat intelligence but is complicated by multilingual content and the presence of multiple named entities on a single page. The system formulates owner identification as a document-level information extraction task, using sophisticated prompting strategies, multimodal augmentation, and self-verification to recognize potential owners. It then employs semantic and string similarity aggregation for entity disambiguation and a hybrid ranking technique to pinpoint the true owner. Evaluated on refined English and newly constructed Chinese datasets containing over 16,000 real websites, OwnerHunter achieved state-of-the-art F1 scores of 0.9505 and 0.9621, respectively, demonstrating superior performance in multilingual environments.
Study Significance: For cybersecurity professionals, this advancement directly enhances capabilities in threat hunting and phishing website detection by providing a more accurate, automated method for attribution, a cornerstone of effective incident response. The framework’s multilingual proficiency and high accuracy address a significant gap in global security operations, where non-English sites often evade traditional analysis. Integrating such LLM-powered tools into Security Operations Center (SOC) workflows and Security Information and Event Management (SIEM) platforms could streamline the initial phases of digital forensics, allowing teams to focus resources on containment and mitigation faster.
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