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Predicting the Discovery Pattern of Publically Known Exploited Vulnerabilities

Movahedi, Y., Cukier, M. and Gashi, I. ORCID: 0000-0002-8017-3184 (2020). Predicting the Discovery Pattern of Publically Known Exploited Vulnerabilities. IEEE Transactions on Dependable and Secure Computing, doi: 10.1109/tdsc.2020.3014872

Abstract

Vulnerabilities with publically known exploits typically form 2-7% 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 paper, 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%.

Publication Type: Article
Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: Software, Mathematical model, Predictive models, Computational modeling, Data models, Software reliability, Security
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
Date Deposited: 17 Aug 2020 12:10
URI: https://openaccess.city.ac.uk/id/eprint/24766
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