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 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 e ff ect 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 e ff ect of vulnerabilities on vendors varies, and greatly depends on the specific software industry. Whereas some industries are shown statistically to be a ff ected negatively by the release of software vulnerabilities, even when those vulnerabilities are not broadly covered by the media, some others were not a ff ected at all.