Measuring the Cost of Software Vulnerabilities

Enterprises are increasingly considering security as an added cost, making it necessary for those enterprises to see a tangible incentive in adopting security measures. Despite data breach laws, prior studies have suggested that only 4% of reported data breach incidents have resulted in litigation i...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Afsah Anwar [verfasserIn] Aminollah Khormali [verfasserIn] Jinchun Choi [verfasserIn] Hisham Alasmary [verfasserIn] Sung Choi [verfasserIn] Saeed Salem [verfasserIn] DaeHun Nyang [verfasserIn] David Mohaisen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	vulnerability economics stock return prediction nvd

Übergeordnetes Werk:	In: EAI Endorsed Transactions on Security and Safety - European Alliance for Innovation (EAI), 2016, 7(2020), 23
Übergeordnetes Werk:	volume:7 ; year:2020 ; number:23

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.4108/eai.13-7-2018.164551

Katalog-ID:	DOAJ005754739

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title	Measuring the Cost of Software Vulnerabilities
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title_auth	Measuring the Cost of Software Vulnerabilities
abstract	Enterprises are increasingly considering security as an added cost, making it necessary for those enterprises to see a tangible incentive in adopting security measures. Despite data breach laws, prior studies have suggested that only 4% of reported data breach incidents have resulted in litigation in federal courts, showing the limited legal ramiﬁcations of security breaches and vulnerabilities. In this paper, we study the hidden cost of software vulnerabilities reported in the National Vulnerability Database (NVD) through stock price analysis. We perform a high-ﬁdelity data augmentation to ensure data reliability and to estimate vulnerability disclosure dates as a baseline for estimating the implication of software vulnerabilities. We further build a model for stock price prediction using the nonlinear autoregressive neural network with exogenous factors (NARX) Neural Network model to estimate the eﬀect of vulnerability disclosure on the stock price. Compared to prior work, which relies on linear regression models, our approach is shown to provide better prediction performance. Our analysis also shows that the eﬀect of vulnerabilities on vendors varies, and greatly depends on the speciﬁc software industry. Whereas some industries are shown statistically to be aﬀected negatively by the release of software vulnerabilities, even when those vulnerabilities are not broadly covered by the media, some others were not aﬀected at all.
abstractGer	Enterprises are increasingly considering security as an added cost, making it necessary for those enterprises to see a tangible incentive in adopting security measures. Despite data breach laws, prior studies have suggested that only 4% of reported data breach incidents have resulted in litigation in federal courts, showing the limited legal ramiﬁcations of security breaches and vulnerabilities. In this paper, we study the hidden cost of software vulnerabilities reported in the National Vulnerability Database (NVD) through stock price analysis. We perform a high-ﬁdelity data augmentation to ensure data reliability and to estimate vulnerability disclosure dates as a baseline for estimating the implication of software vulnerabilities. We further build a model for stock price prediction using the nonlinear autoregressive neural network with exogenous factors (NARX) Neural Network model to estimate the eﬀect of vulnerability disclosure on the stock price. Compared to prior work, which relies on linear regression models, our approach is shown to provide better prediction performance. Our analysis also shows that the eﬀect of vulnerabilities on vendors varies, and greatly depends on the speciﬁc software industry. Whereas some industries are shown statistically to be aﬀected negatively by the release of software vulnerabilities, even when those vulnerabilities are not broadly covered by the media, some others were not aﬀected at all.
abstract_unstemmed	Enterprises are increasingly considering security as an added cost, making it necessary for those enterprises to see a tangible incentive in adopting security measures. Despite data breach laws, prior studies have suggested that only 4% of reported data breach incidents have resulted in litigation in federal courts, showing the limited legal ramiﬁcations of security breaches and vulnerabilities. In this paper, we study the hidden cost of software vulnerabilities reported in the National Vulnerability Database (NVD) through stock price analysis. We perform a high-ﬁdelity data augmentation to ensure data reliability and to estimate vulnerability disclosure dates as a baseline for estimating the implication of software vulnerabilities. We further build a model for stock price prediction using the nonlinear autoregressive neural network with exogenous factors (NARX) Neural Network model to estimate the eﬀect of vulnerability disclosure on the stock price. Compared to prior work, which relies on linear regression models, our approach is shown to provide better prediction performance. Our analysis also shows that the eﬀect of vulnerabilities on vendors varies, and greatly depends on the speciﬁc software industry. Whereas some industries are shown statistically to be aﬀected negatively by the release of software vulnerabilities, even when those vulnerabilities are not broadly covered by the media, some others were not aﬀected at all.
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