PACE: Pattern accurate computationally efficient bootstrapping for timely discovery of cyber-security concepts
Document Type
Conference Proceeding
Publication Date
1-1-2013
Abstract
Public disclosure of important security information, such as knowledge of vulnerabilities or exploits, often occurs in blogs, tweets, mailing lists, and other online sources significantly before proper classification into structured databases. In order to facilitate timely discovery of such knowledge, we propose a novel semi-supervised learning algorithm, PACE, for identifying and classifying relevant entities in text sources. The main contribution of this paper is an enhancement of the traditional bootstrapping method for entity extraction by employing a time-memory trade-off that simultaneously circumvents a costly corpus search while strengthening pattern nomination, which should increase accuracy. An implementation in the cyber-security domain is discussed as well as challenges to Natural Language Processing imposed by the security domain. © 2013 IEEE.
Recommended Citation
McNeil, Nikki; Bridges, Robert A.; Iannacone, Michael D.; Czejdo, Bogdan; Perez, Nicolas; and Goodall, John R., "PACE: Pattern accurate computationally efficient bootstrapping for timely discovery of cyber-security concepts" (2013). College of Health, Science, and Technology. 1075.
https://digitalcommons.uncfsu.edu/college_health_science_technology/1075