An Evaluation Of N-gram System Call Sequence In Mobile Malware Detection
The rapid growth of Android-based mobile devices technology in recent years has increased the proliferation of mobile devices throughout the community at large. The ability of Android mobile devices has become similar to its desktop environment; users can do more than just a phone call and short tex...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Asian Research Publishing Network (ARPN)
2016
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/16991/2/zaki.pdf http://eprints.utem.edu.my/id/eprint/16991/ http://www.arpnjournals.org/jeas/research_papers/rp_2016/jeas_0316_3766.pdf |
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| Summary: | The rapid growth of Android-based mobile devices technology in recent years has increased the proliferation of mobile devices throughout the community at large. The ability of Android mobile devices has become similar to its desktop environment; users can do more than just a phone call and short text messaging. These days, Android mobile devices are used for various applications such as web browsing, ubiquitous services, social networking, MMS and many more. However, the rapid growth of Android mobile devices technology has also triggered the malware author to start
exploiting the vulnerabilities of the devices. Based on this reason, this paper explores mobile malware detection through an n-gram system call sequence which uses a sequence of system call invoked by the mobile application as the feature in classifying a benign and malicious mobile application. Several n-gram values are evaluated with Linear-SVM classifier to determine the best n system call sequence that produces the highest detection accuracy and highest True Positive Rate (TPR) with low False Positive Rate (FPR). |
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