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arXiv · 1903.01618

Detecting and Classifying Android Malware using Static Analysis along with Creator Information

Hyunjae Kang·Jae-wook Jang·Aziz Mohaisen·Huy Kang Kim
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Abstract

Thousands of malicious applications targeting mobile devices, including the popular Android platform, are created every day. A large number of those applications are created by a small number of professional under-ground actors, however previous studies overlooked such information as a feature in detecting and classifying malware, and in attributing malware to creators. Guided by this insight, we propose a method to improve on the performance of Android malware detection by incorporating the creator's information as a feature and classify malicious applications into similar groups. We developed a system that implements this method in practice. Our system enables fast detection of malware by using creator information such as serial number of certificate. Additionally, it analyzes malicious be-haviors and permissions to increase detection accuracy. The system also can classify malware based on similarity scoring. Finally, we showed detection and classification performance with 98% and 90% accuracy respectively.

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Hyunjae Kang, Jae-wook Jang, Aziz Mohaisen, Huy Kang Kim. 2019-03-02. Detecting and Classifying Android Malware using Static Analysis along with Creator Information. https://doi.org/10.1155/2015%2F479174

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