Towards Automatically Matching Security Advisories to CPEs: String Similarity-based Vendor Matching

Conference · Wed Aug 20 04:00:00 EDT 2025

DOI:https://doi.org/10.1109/ICNC59896.2024.10556231· OSTI ID:2584215

McClanahan, Kylie; Li, Qinghua

When a vulnerability is reported by the National Vulnerability Database (NVD), affected products are listed in the structured Common Platform Enumeration (CPE) format. Unfortunately, if the vulnerability is in a software library (e.g., Log4j), it will not include CPEs for each product containing that library. In these cases, security operators need to manually read the vendor's or third-party security advisories to see if their product is affected. However, these advisories do not report affected products in a structured format, which prevents automated processing, This paper makes the first effort towards automatically constructing structured CPEs for the vulnerable products in a non-NVD security advisory from the unstructured data in the advisory. Since this is a very challenging problem, this paper specifically focuses on the initial but key step of matching the un-structured vendor names in security advisories to the structured vendor representations in the standard CPE format. We explore the feasibility of using string similarity to solve the problem. The basic idea is to compare a vendor name from the non-NVD advisory with each vendor in the official CPE dictionary. The CPE vendor with the highest similarity score to the advisory's vendor will be considered as the match. We first conduct an experimental, comparative study of multiple mainstream string similarity metrics for this matching problem. To improve the performance, we then design a new string similarity metric that is adapted from an existing metric by weighing different tokens in the advisory's vendor name differently.

• Research Organization: University of Arkansas
• Sponsoring Organization: Department of Energy
• DOE Contract Number: CR0000003
• OSTI ID: 2584215
• Country of Publication: United States
• Language: English