标题： EventHunter：从黑客论坛讨论中动态聚类和排序安全事件 显示英文标题

标题： EventHunter: Dynamic Clustering and Ranking of Security Events from Hacker Forum Discussions

摘要： 黑客论坛为新兴网络安全威胁提供了关键的早期预警信号，但从其非结构化和嘈杂的内容中提取可操作的情报仍然是一个重大挑战。 本文提出了一种无监督框架，能够自动检测、聚类并优先排序黑客论坛帖子中讨论的安全事件。 我们的方法利用经过对比学习微调的基于Transformer的嵌入，将相关讨论分组到不同的安全事件集群中，无需依赖预定义的关键字即可识别如零日披露或恶意软件发布等事件。 该框架包含一种每日排名机制，使用反映及时性、来源可信度、信息完整性和相关性的可量化指标来优先排序已识别的事件。 在真实世界黑客论坛数据上的实验评估表明，我们的方法能有效减少噪声并凸显高优先级威胁，使安全分析师能够采取主动响应。 通过将分散的黑客论坛讨论转化为结构化、可操作的情报，我们的工作解决了自动化威胁检测和分析中的基本挑战。 显示英文摘要

摘要： Hacker forums provide critical early warning signals for emerging cybersecurity threats, but extracting actionable intelligence from their unstructured and noisy content remains a significant challenge. This paper presents an unsupervised framework that automatically detects, clusters, and prioritizes security events discussed across hacker forum posts. Our approach leverages Transformer-based embeddings fine-tuned with contrastive learning to group related discussions into distinct security event clusters, identifying incidents like zero-day disclosures or malware releases without relying on predefined keywords. The framework incorporates a daily ranking mechanism that prioritizes identified events using quantifiable metrics reflecting timeliness, source credibility, information completeness, and relevance. Experimental evaluation on real-world hacker forum data demonstrates that our method effectively reduces noise and surfaces high-priority threats, enabling security analysts to mount proactive responses. By transforming disparate hacker forum discussions into structured, actionable intelligence, our work addresses fundamental challenges in automated threat detection and analysis.