Identification of Software Security Topics using Similarity of Embedding Vectors of Keywords
Phong Minh Vu, Tung Thanh Nguyen
Journal of Information Security Research, Vol. 16, No. 2, 2025.
DOI: https://doi.org/10.6025/jisr/2025/16/2/53-60
Link: https://www.dline.info/jisr/fulltext/v16n2/jisrv16n2_2.pdf
Abstract:
Software security incidents occur every day and thousands of software security reports are announced each month. Thus, it is difficult for software security researchers, engineers, and other stakeholders to follow software security topics of their interests in real-time. In this paper, we propose, SOSK, a novel tool for this problem. SOSK allows a user to import a collection of software security reports. It pre-processes and extracts the most important keywords from the textual description of the reports. Based on the similarity of embedding vectors of keywords, SOSK can expand and/or refine a keyword set from a much smaller set of user provided keywords. Thus, SOSK allows users to define any topic of their interests and retrieve security reports relevant to that topic effectively. Our preliminary evaluation shows that SOSK can expand keywords and retrieve reports relevant to user requests.