Abstract
Online vulnerability databases provide a wealth of information pertaining to vulnerabilities that are present in computer application software, operating systems, and firmware. Extracting useful information from these databases that can subsequently be utilized by applications such as vulnerability scanners and security monitoring tools can be a challenging task. This paper presents two approaches to information extraction from online vulnerability databases: a machine learning based solution and a solution that exploits linguistic patterns elucidated by part-of-speech tagging. These two systems are evaluated to compare accuracy in recognizing security concepts in previously unseen vulnerability description texts. We discuss design considerations that should be taken into account in implementing information retrieval systems for security domain.
This material is based upon work supported by the National Science Foundation under Grant No. 0905232.
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Weerawardhana, S., Mukherjee, S., Ray, I., Howe, A. (2015). Automated Extraction of Vulnerability Information for Home Computer Security. In: Cuppens, F., Garcia-Alfaro, J., Zincir Heywood, N., Fong, P. (eds) Foundations and Practice of Security. FPS 2014. Lecture Notes in Computer Science(), vol 8930. Springer, Cham. https://doi.org/10.1007/978-3-319-17040-4_24
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DOI: https://doi.org/10.1007/978-3-319-17040-4_24
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