Predicting the Discovery Pattern of Publically Known Exploited Vulnerabilities

• Published in: IEEE transactions on dependable and secure computing Vol. 19; no. 2; pp. 1181 - 1193
• Main Authors: Movahedi, Yazdan, Cukier, Michel, Gashi, Ilir
• Format: Journal Article
• Language: English
• Published: Washington IEEE 01.03.2022; IEEE Computer Society
• Subjects: all vulnerabilities; artificial neural network; Computational modeling; Data models; exploited vulnerabilities; Mathematical model; Neural networks; Prediction; Predictive models; Security; Software; Software reliability; time to next vulnerability; Vulnerability; vulnerability discovery models
• ISSN: 1545-5971; 1941-0018
• DOI: 10.1109/TDSC.2020.3014872

Vulnerabilities with publically known exploits typically form 2-7 percent of all vulnerabilities reported for a given software version. With a smaller number of known exploited vulnerabilities compared with the total number of vulnerabilities, it is more difficult to model and predict when a vulnerability with a known exploit will be reported. In this article, we introduce an approach for predicting the discovery pattern of publically known exploited vulnerabilities using all publically known vulnerabilities reported for a given software. Eight commonly used vulnerability discovery models (VDMs) and one neural network model (NNM) were utilized to evaluate the prediction capability of our approach. We compared their predictions results with the scenario when only exploited vulnerabilities were used for prediction. Our results show that, in terms of prediction accuracy, out of eight software we analyzed, our approach led to more accurate results in seven cases. Only in one case, the accuracy of our approach was worse by 1.6 percent.