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Multivariate models using MCMCBayes for web-browser vulnerability discovery
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DOI：10.1016/j.ress.2018.03.024.png)
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Abstract
En 中文
Vulnerabilities that enable well-known exploit techniques are preventable, but their public discovery continues in software. Vulnerability discovery modeling (VDM) techniques were proposed to assist managers with decisions, but do not include influential variables describing the software release (SR) (e.g., code size and complexity characteristics) and security assessment profile (SAP) (e.g., security team size or skill). Consequently, they have been limited to modeling discoveries over time for SR and SAP scenarios of unique products, whose results are not readily comparable without making assumptions that equate all SR and SAP combinations under study. This article introduces a groundbreaking capability that allows forecasting expected discoveries over time for arbitrary SR and SAP combinations, thus enabling managers to better understand the effects of influential variables they control on the phenomenon. To do this, we use variables that describe arbitrary SR and SAP combinations and construct VDM extensions that parametrically scale results from a defined baseline SR and SAP to the arbitrary SR and SAP of interest. Scaling parameters are estimated using expert judgment data gathered with a novel pairwise comparison approach. These data are then used to demonstrate predictions and how multivariate VDM techniques could be used by software-makers.
Keywords:
Parametric scaling
Regression
Growth curve
Poisson process
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