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pDroid

Date Issued
August 1, 2016
Author(s)
Allen, Joe Larry  
Advisor(s)
Jinyuan Sun
Additional Advisor(s)
Qing Cao
Michael Jantz
Permanent URI
https://trace.tennessee.edu/handle/20.500.14382/40120
Abstract

When an end user attempts to download an app on the Google Play Store they receive two related items that can be used to assess the potential threats of an application, the list of permissions used by the application and the textual description of the application. However, this raises several concerns. First, applications tend to use more permissions than they need and end users are not tech-savvy enough to fully understand the security risks. Therefore, it is challenging to assess the threats of an application fully by only seeing the permissions. On the other hand, most textual descriptions do not clearly define why they need a particular permission. These two issues conjoined make it difficult for end users to accurately assess the security threats of an application. This has lead to a demand for a framework that can accurately determine if a textual description adequately describes the actual behavior of an application. In this Master Thesis, we present pDroid (short for privateDroid), a market-independent framework that can compare an Android application’s textual description to its internal behavior. We evaluated pDroid using 1562 benign apps and 243 malware samples, and pDroid correctly classified 91.4% of malware with a false positive rate of 4.9%.

Subjects

Android Security

Static Analysis

Text Mining

App Mining

Malware Classificaito...

Disciplines
Information Security
Software Engineering
Degree
Master of Science
Major
Computer Engineering
Embargo Date
January 1, 2011
File(s)
Thumbnail Image
Name

0-jallen89_pDroid_thesis.pdf

Size

1.01 MB

Format

Adobe PDF

Checksum (MD5)

743cd3e0ab78083e3288ed1cbc054356

Thumbnail Image
Name

jallen89_pDroid_thesis.pdf

Size

1.01 MB

Format

Adobe PDF

Checksum (MD5)

743cd3e0ab78083e3288ed1cbc054356


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