GapFinder: Finding Inconsistency of Security Information From Unstructured Text
Hyeonseong Jo, Jinwoo Kim, Phillip Porras, Vinod Yegneswaran, Seungwon Shin
Abstract
Textual data mining of open source intelligence on the Web has become an increasingly important topic across a wide range of domains such as business, law enforcement, military, and cybersecurity. Text mining efforts utilize natural language processing to transform unstructured web content into structured forms that can drive various machine learning applications and data indexing services. For example, applications for text mining in cybersecurity have produced a range of threat intelligence services that serve the IT industry. However, a less studied problem is that of automating the identification of semantic inconsistencies among various text input sources. In this paper, we introduce GapFinder, a new inconsistency checking system for identifying semantic inconsistencies within the cybersecurity domain. Specifically, we examine the problem of identifying technical inconsistencies that arise in the functional descriptions of open source malware threat reporting information. Our evaluation, using tens of thousands of relations derived from web-based malware threat reports, demonstrates the ability of GapFinder to identify the presence of inconsistencies.