Modeling and Analysis of Intentional And Unintentional Security Vulnerabilities in a Mobile Platform Page: 31
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Package Name Group Label
android.graphics Graphics
android.graphics.drawable Graphics
android.graphics.drawable.shapes Graphics
android.hardware Input
android.hardware.input Input
TABLE 4.3. Some Android API packages and their hand labels.
4.3.2. Method 2 (API Calls + Character Frequency)
Modifying the previous approach of feature extraction helps to include more features.
The extended feature extraction implementation not only scans the java source files but also
browses through the content of AndroidManifest . xml and String. xml. I extracted
and retained all the attribute and tag values from the Andro i dMan iftest . xml, except those
tagged under <uses-permission />, <permission />, <permission-tree />,
and <pe rmi s s ion-group />. Thus, the machine learning models does not train any informa-
tion with respect to the permissions. Post scrapping, the selected attribute information is considered
as a long string and treated to calculate the character frequency for both the xml content in order
to add them into the feature vector.
4.3.3. Method 3 (API Calls + Dictionary Based)
This method being an extension of Method 1, utilizes the scalar vectors along with building
a dictionary of words and grouping them into a root word, instead of just counting the characters
as described in Table 4.4. Selecting a key word is very critical and plays an important role in
the implementation of this method. I researched a couple of applications at the Google Play to
see what type of words best describe different categories of apps to carefully select key words and
group them into buckets. Then I extracted the XML file as described in Method 2, in order to check
the occurrence of the selected keywords in those XML files. If a keyword is found, the count of
the bucket that key word belongs is increased by 1. Thus, on counting all the occurrences of the31
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Mohamed Issadeen, Mohamed Fazeen. Modeling and Analysis of Intentional And Unintentional Security Vulnerabilities in a Mobile Platform, dissertation, December 2014; Denton, Texas. (https://digital.library.unt.edu/ark:/67531/metadc700067/m1/45/: accessed July 18, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .