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Leveraging the Use of API Call Traces for Mobile Security


Leveraging the Use of API Call Traces for Mobile Security

Khanmohammadi, Kobra (2019) Leveraging the Use of API Call Traces for Mobile Security. PhD thesis, Concordia University.

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The growing popularity of Android applications has generated increased concerns over the danger of piracy and the spread of malware. A popular way to distribute malware in the mobile world is through the repackaging of legitimate apps. This process consists of downloading, unpacking, manipulating, recompiling an application, and publishing it again in an app store. In this thesis, we conduct an empirical study of over 15,000 apps to gain insights into the factors that drive the spread of repackaged apps. We also examine the motivations of developers who publish repackaged apps and those of users who download them, as well as the factors that determine which apps are chosen for repackaging, and the ways in which the apps are modified during the repackaging process. We have also studied android applications structure to investigate the locations where malicious code are more probable to be embedded into legitimate applications. We observed that service components contain key characteristics that entice attackers to misuse them. Therefore, we have focus on studying the behavior of malicious and benign services. Whereas benign services tend to inform the user of the background operations, malicious services tend to do long running operations and have a loose connection with rest of the code. These findings lead us to propose an approach to detect malware by studying the services’ behavior. To model the services’ behavior, we used API calls as feature sets. We proposed a hybrid approach using static and dynamic analysis to extract the API calls through the service lifecycle. Finally, we used the list of API calls preponderantly present in both malware as well as benign services as the feature set. We applied machine learning algorithms to use the feature set to classify malicious services and benign services.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science
Item Type:Thesis (PhD)
Authors:Khanmohammadi, Kobra
Institution:Concordia University
Degree Name:Ph. D.
Program:Electrical and Computer Engineering
Date:21 October 2019
Thesis Supervisor(s):Hamou-Lhadj, Abdelwahab and Khoury, Raphaël
ID Code:986500
Deposited On:25 Jun 2020 18:44
Last Modified:25 Jun 2020 18:44


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