2021/01/05 by Jiamou Sun, Sun, Jiamou, Zhenchang Xing +11 · 1 citation
Computer Science · #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Natural Language Processing Techniques #Topic Modeling #Web Application Security Vulnerabilities
paper · pdf · doi:10.48550/arxiv.2101.01431
openalex publication_date 2021/01/05 · openalex created_date 2021/01/18 · openalex updated_date 2026/07/28
ExploitDB is one of the important public websites, which contributes a large number of vulnerabilities to official CVE database. Over 60% of these vulnerabilities have high- or critical-security risks. Unfortunately, over 73% of exploits appear publicly earlier than the corresponding CVEs, and about 40% of exploits do not even have CVEs. To assist in documenting CVEs for the ExploitDB posts, we propose an open information method to extract 9 key vulnerability aspects (vulnerable product/version/component, vulnerability type, vendor, attacker type, root cause, attack vector and impact) from the verbose and noisy ExploitDB posts. The extracted aspects from an ExploitDB post are then composed into a CVE description according to the suggested CVE description templates, which is must-provided information for requesting new CVEs. Through the evaluation on 13,017 manually labeled sentences and the statistically sampling of 3,456 extracted aspects, we confirm the high accuracy of our extraction method. Compared with 27,230 reference CVE descriptions. Our composed CVE descriptions achieve high ROUGH-L (0.38), a longest common subsequence based metric for evaluating text summarization methods.