标题： LRCTI：一种基于大语言模型的多步骤证据检索与推理框架，用于网络威胁情报可信度验证 显示英文标题

标题： LRCTI: A Large Language Model-Based Framework for Multi-Step Evidence Retrieval and Reasoning in Cyber Threat Intelligence Credibility Verification

摘要： 验证网络威胁情报（CTI）的可信度对于可靠的网络安全防御至关重要。然而，传统方法通常将此任务视为静态分类问题，依赖于手工设计的特征或孤立的深度学习模型。这些方法在处理不完整、异构或嘈杂的情报时往往缺乏所需的鲁棒性，并且在决策过程中提供有限的透明度，这会降低它们在现实威胁环境中的有效性。为解决这些限制，我们提出了LRCTI，这是一种基于大型语言模型（LLM）的框架，旨在进行多步骤的CTI可信度验证。该框架首先使用文本摘要模块，将复杂的情报报告提炼成简洁且可操作的威胁声明。然后，它使用自适应多步骤证据检索机制，通过LLM反馈引导，迭代地从特定于CTI的语料库中识别和精炼支持信息。最后，应用基于提示的自然语言推理（NLI）模块来评估每个声明的可信度，并为分类结果生成可解释的依据。在两个基准数据集CTI-200和PolitiFact上进行的实验表明，与最先进的基线相比，LRCTI的F1-Macro和F1-Micro分数提高了超过5%，分别达到90.9%和93.6%。这些结果表明，LRCTI有效解决了先前方法的核心局限性，为自动化的CTI可信度验证提供了可扩展、准确且可解释的解决方案。 显示英文摘要

摘要： Verifying the credibility of Cyber Threat Intelligence (CTI) is essential for reliable cybersecurity defense. However, traditional approaches typically treat this task as a static classification problem, relying on handcrafted features or isolated deep learning models. These methods often lack the robustness needed to handle incomplete, heterogeneous, or noisy intelligence, and they provide limited transparency in decision-making-factors that reduce their effectiveness in real-world threat environments. To address these limitations, we propose LRCTI, a Large Language Model (LLM)-based framework designed for multi-step CTI credibility verification. The framework first employs a text summarization module to distill complex intelligence reports into concise and actionable threat claims. It then uses an adaptive multi-step evidence retrieval mechanism that iteratively identifies and refines supporting information from a CTI-specific corpus, guided by LLM feedback. Finally, a prompt-based Natural Language Inference (NLI) module is applied to evaluate the credibility of each claim while generating interpretable justifications for the classification outcome. Experiments conducted on two benchmark datasets, CTI-200 and PolitiFact show that LRCTI improves F1-Macro and F1-Micro scores by over 5%, reaching 90.9% and 93.6%, respectively, compared to state-of-the-art baselines. These results demonstrate that LRCTI effectively addresses the core limitations of prior methods, offering a scalable, accurate, and explainable solution for automated CTI credibility verification