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Vulnerability Prediction Capability: A Comparison between Vulnerability Discovery Models and Neural Network Models

Mohavedi, Y., Cukier, M. and Gashi, I. ORCID: 0000-0002-8017-3184 (2019). Vulnerability Prediction Capability: A Comparison between Vulnerability Discovery Models and Neural Network Models. Computers and Security, doi: 10.1016/j.cose.2019.101596

Abstract

In this paper, we introduce an approach for predicting the cumulative number of software vulnerabilities that is in most cases more accurate than vulnerability discovery models (VDMs). Our approach uses a neural network model (NNM) to model the nonlinearities associated with vulnerability disclosure. Nine common VDMs were used to compare their prediction capability with our approach. The different models were applied to vulnerabilities associated with eight well-known software (four operating systems and four web browsers). The models were assessed in terms of prediction accuracy and prediction bias. Out of eight software we analyzed, the NNM outperformed the VDMs in all the cases in terms of prediction accuracy, and provided smaller values of absolute average bias in seven cases. This study shows that NNMs are promising for accurate predictions of software vulnerabilities disclosures.

Publication Type: Article
Publisher Keywords: Vulnerability Discovery Model, Neural Network Model, Time Series, Vulnerability Discovery Process, Prediction, Software Reliability
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
School of Mathematics, Computer Science & Engineering > Computer Science > Software Reliability
URI: http://openaccess.city.ac.uk/id/eprint/22680
[img] Text - Accepted Version
This document is not freely accessible until 22 August 2020 due to copyright restrictions.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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