To adapt to a constantly evolving landscape of cyber threats, organizations actively need to collect Indicators of Compromise (IOCs), i.e., forensic artifacts that signal that a host or network might have been compromised. IOCs can be collected through open-source and commercial structured IOC feeds. But, they can also be extracted from a myriad of unstructured threat reports written in natural language and distributed using a wide array of sources such as blogs and social media. This work presents GoodFATR an automated platform for collecting threat reports from a wealth of sources and extracting IOCs from them. GoodFATR supports 6 sources: RSS, Twitter, Telegram, Malpedia, APTnotes, and ChainSmith. GoodFATR continuously monitors the sources, downloads new threat reports, extracts 41 indicator types from the collected reports, and filters generic indicators to output the IOCs. We propose a novel majority-vote methodology for evaluating the accuracy of indicator extraction tools, and apply it to compare 7 popular tools with GoodFATR's indicator extraction module. We run GoodFATR over 15 months to collect 472,891 reports from the 6 sources; extract 1,043,932 indicators from the reports; and identify 655,971 IOCs. We analyze the collected data to identify the top IOC contributors and the IOC class distribution. Finally, we present a case study on how GoodFATR can assist in tracking cybercrime relations on the Bitcoin blockchain.
翻译:为了适应不断演变的网络威胁,各组织积极需要收集 " 妥协指标 " (IOCs),即表明主机或网络可能受到损害的法医文物;国际奥委会可以通过开放源码和商业结构化的海委会资料收集;但是,也可以从以自然语言编写并使用诸如博客和社会媒体等广泛来源分发的各种无结构的威胁报告中提取;这项工作为GoodFaTR提供了一个从大量来源收集威胁报告的自动化平台,并从中提取海委会指标。国际奥联支持6个来源:RSS、Twitter、Telegram、Malpedia、APT Notes和链Smith。国际奥联不断监测来源,下载新的威胁报告,从所收集的报告中提取41个指标类型,并过滤通用指标,用于评价指标提取工具的准确性,并将其用于将7种流行工具与全球奥委会指标提取模块进行比较。我们运行了15个月,从6个来源收集了472 TRsblock、891块报告,从6个来源提取了1 093号海委会报告,从我们从海委会数据库中检索了1 BBIA数据库中确定了1号数据。