Vulnerability Discovery and Intelligence
Automated CVE Detection & Characterization
Security professionals rely on Common Vulnerability and Exposure (CVE) reports, to make sure disclosed vulnerabilities are not exploited by attackers. Similarly, the security research community relies on CVEs to conduct empirical studies as well as develop and validate their techniques. There are public vulnerability database systems that score, characterize and publish disclosed
vulnerabilities. However, prior research has raised serious concerns about the competency of these systems and the consistency of the data and characterization services they provide. An automated end-to-end approach is needed that can automatically characterize vulnerabilities, thereby reducing the manual effort needed for CVE management. We design and develop NVIP- a new, automated, configurable, and portable software platform that provides near real-time publishing of vulnerabilities and incorporates intelligent analysis and characterization services using Machine Learning (ML) and novel Information Theoretical (IT) methods. It characterizes each software vulnerability in five domains defined in the NIST Vulnerability Description Ontology (VDO) framework the Attack Theater, Context, Impact Method, Logical Impact, and Mitigation. Experiment results indicate that software vulnerabilities can be detected up to 95 hours earlier than the US National Vulnerability Database, and using a variety of ML/IT methods, CVEs can be characterized with F-Measure values up to 1.
Figure 2: An overview of the NVIP.
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