sesa 20(23): e1

Research Article

Measuring the Cost of Software Vulnerabilities

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  • @ARTICLE{10.4108/eai.13-7-2018.164551,
        author={Afsah Anwar and Aminollah Khormali and Jinchun Choi and Hisham Alasmary and Sung J. Choi and Saeed Salem and DaeHun Nyang and David Mohaisen},
        title={Measuring the Cost of Software Vulnerabilities},
        journal={EAI Endorsed Transactions on Security and Safety},
        keywords={Vulnerability Economics, Stock Return Prediction, NVD},
  • Afsah Anwar
    Aminollah Khormali
    Jinchun Choi
    Hisham Alasmary
    Sung J. Choi
    Saeed Salem
    DaeHun Nyang
    David Mohaisen
    Year: 2020
    Measuring the Cost of Software Vulnerabilities
    DOI: 10.4108/eai.13-7-2018.164551
Afsah Anwar1,*, Aminollah Khormali1, Jinchun Choi1,2, Hisham Alasmary1,3, Sung J. Choi1, Saeed Salem4, DaeHun Nyang5, David Mohaisen1
  • 1: University of Central Florida, Orlando, FL 32816, USA
  • 2: Inha University, Incheon, Republic of Korea
  • 3: King Khalid University, Abha, Saudi Arabia
  • 4: North Dakota State University, Fargo, ND, USA
  • 5: Ewha Womans University, Seoul, Republic of Korea
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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.