Ontology Based Security Threat Assessment and Mitigation for Cloud Systems Page: 77
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been used to measure the complexity of a software product that are known to correlate with
software bugs. In addition, we collect the number of security vulnerabilities reported with
each release of the software.
For the IT software product under consideration, we start by collecting details about
the product's releases (with an identifying name), the number of releases and the number of
vulnerabilities already reported. We analyze the source code of each released version of the
product and collect various software metrics for source codes of each version. The collected
data is stored in a dataset file as comma-separated values (CSV). The following describes
the process of collecting data.
(1) Select the IT Product to analyze.
(2) Download all releases of this IT product's source code for each version.
(3) For each release, represent it using a Common Platform Enumeration (CPE) [83]
format as its unique identifier for cataloging the product version and its data into
our datasets.
(4) For each release, discover all reported vulnerabilities using VULCAN indexing schemes
(See. Section 4.2.1.5) or IKAWAFARM API (See. Section 3.4.1).
(5) Analyze the source code for each version and obtain software complexity metrics
using available tools such as Understand [64].
Our SWEEP toolkit automates all the steps described.
4.4.2.2. Predictive Model
The research question we originally proposed was whether we could predict the num-
ber of undisclosed vulnerabilities for any given software product. We base our solution on
the data that can be collected for this software product using our SWEEP toolkit as dis-
cussed in the previous Section 4.4.2.1. Once a dataset is generated for the software product
of interest, we use a machine learning regression classifier to build a model that can predict
the number of vulnerabilities contained in a specific release of the software under evaluation.
The accuracy of prediction depends on the amount of data, since the more data we77
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Kamongi, Patrick. Ontology Based Security Threat Assessment and Mitigation for Cloud Systems, dissertation, December 2018; Denton, Texas. (https://digital.library.unt.edu/ark:/67531/metadc1404576/m1/90/: accessed July 17, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .