Current Search: Peiravian, Naser (x)
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Title
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Data mining heuristic-¬based malware detection for android applications.
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Creator
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Peiravian, Naser, Zhu, Xingquan, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
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Abstract/Description
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The Google Android mobile phone platform is one of the dominant smartphone operating systems on the market. The open source Android platform allows developers to take full advantage of the mobile operation system, but also raises significant issues related to malicious applications (Apps). The popularity of Android platform draws attention of many developers which also attracts the attention of cybercriminals to develop different kinds of malware to be inserted into the Google Android Market...
Show moreThe Google Android mobile phone platform is one of the dominant smartphone operating systems on the market. The open source Android platform allows developers to take full advantage of the mobile operation system, but also raises significant issues related to malicious applications (Apps). The popularity of Android platform draws attention of many developers which also attracts the attention of cybercriminals to develop different kinds of malware to be inserted into the Google Android Market or other third party markets as safe applications. In this thesis, we propose to combine permission, API (Application Program Interface) calls and function calls to build a Heuristic-Based framework for the detection of malicious Android Apps. In our design, the permission is extracted from each App’s profile information and the APIs are extracted from the packed App file by using packages and classes to represent API calls. By using permissions, API calls and function calls as features to characterize each of Apps, we can develop a classifier by data mining techniques to identify whether an App is potentially malicious or not. An inherent advantage of our method is that it does not need to involve any dynamic tracking of the system calls but only uses simple static analysis to find system functions from each App. In addition, Our Method can be generalized to all mobile applications due to the fact that APIs and function calls are always present for mobile Apps. Experiments on real-world Apps with more than 1200 malwares and 1200 benign samples validate the algorithm performance. Research paper published based on the work reported in this thesis: Naser Peiravian, Xingquan Zhu, Machine Learning for Android Malware Detection Using Permission and API Calls, in Proc. of the 25th IEEE International Conference on Tools with Artificial Intelligence (ICTAI) – Washington D.C, November 4-6, 2013.
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Date Issued
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2013
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PURL
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http://purl.flvc.org/fau/fd/FA0004045
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Subject Headings
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Computer networks -- Security measures, Data encryption (Computer science), Data structures (Computer science), Internet -- Security measures
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Format
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Document (PDF)
