Ads (advertisements) are the main economic source for most free web content. Modern ads are not only a large nuisance to end users but also an often violation of their privacy via tracking methods. There has been a rise in the use of ad blocking software. The major problem with these ad blocking software is that they rely on manually generated blacklists. In other words, humans need to detect ad URLs (Uniform Resource Locator) and add them to a blacklist so that it can be used by the ad blocking software. The purpose of this project is to design and implement an automated ad blocking software for Android mobile devices that does not rely on manually generated blacklists. The hypothesis to automate the generation of the blacklist is that URLs, which are not present in a given comprehensive whitelist and are visited more than a certain threshold number, are likely to be ad URLs.
In order to test the hypothesis, an Android mobile application is developed without requiring the root access on the device. The mobile application uses a local VPN (Virtual Private Network) server to capture the entire network traffic of the mobile device. The application is installed on a number of Android devices to collect the data to test the hypothesis.
The experiments illustrate that a false positive rate of 0.1% and a false negative rate of 0.26% can be achieved by an optimal frequency threshold number. It is concluded that the URLs that are not present in a given comprehensive whitelist and that are visited with higher frequencies are more likely to be ad URLs.
|Commitee:||Aliasgari, Mehrdad, Englert, Burkhard, Terrell, Neal|
|School:||California State University, Long Beach|
|Department:||Computer Engineering and Computer Science|
|School Location:||United States -- California|
|Source:||MAI 56/02M(E), Masters Abstracts International|
|Keywords:||Android, Mobile devices|
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