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Vulnerability prediction capability: A comparison between vulnerability discovery models and neural network models

机译：漏洞预测能力：漏洞发现模型与神经网络模型之间的比较

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摘要

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. (C) 2019 Elsevier Ltd. All rights reserved.
机译：在本文中，我们介绍了一种预测软件漏洞累积数量的方法，该方法在大多数情况下比漏洞发现模型（VDM）更准确。我们的方法使用神经网络模型（NNM）来建模与漏洞披露相关的非线性。使用九种常见的VDM将其预测能力与我们的方法进行比较。将不同的模型应用于与八个知名软件（四个操作系统和四个Web浏览器）相关的漏洞。根据预测准确性和预测偏差对模型进行了评估。在我们分析的八种软件中，NNM在所有情况下的预测准确性均优于VDM，并且在七种情况下提供的绝对平均偏差值较小。这项研究表明，NNM有望准确预测软件漏洞的披露。 （C）2019 Elsevier Ltd.保留所有权利。

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