An Image-inspired and CNN-based Android Malware Detection Approach
Wed 13 Nov 2019 11:10 - 11:25 at South Park - Student Research Competition - Selected Presentations (Graduate) Chair(s): Jie M. Zhang, Jin L.C. Guo
Until 2017, Android smartphones occupied approximately 87% of the smartphone market. The vast market also promotes the development of Android malware. Nowadays, the number of malware targeting Android devices found daily is more than 38,000. With the rapid progress of mobile application programming and anti-reverse-engineering techniques, it is harder to detect all kinds of malware. To address challenges in existing detection techniques, such as data obfuscation and limited codes coverage, we propose a detection approach that directly learns features of malware from Dalvik bytecode based on deep learning technique (CNN). The average detection time of our model is 0.22 seconds, which is much lower than other existing detection approaches. In the meantime, the overall accuracy of our model achieves over 89%.
Tue 12 Nov
Wed 13 Nov
10:40 - 12:20: Student Research Competition - Student Research Competition - Selected Presentations (Graduate) at South Park Chair(s): Jie M. ZhangUniversity College London, UK, Jin L.C. GuoMcGill University | ||||||||||||||||||||||||||||||||||||||||||
10:40 - 10:55 | Toward Practical Automatic Program Repair Ali GhanbariThe University of Texas at Dallas | |||||||||||||||||||||||||||||||||||||||||
10:55 - 11:10 | Verifying Determinism in Sequential Programs Rashmi MudduluruUniversity of Washington, Seattle | |||||||||||||||||||||||||||||||||||||||||
11:10 - 11:25 | An Image-inspired and CNN-based Android Malware Detection Approach Shao YangCase Western Reserve University | |||||||||||||||||||||||||||||||||||||||||
11:25 - 11:40 | User Preference Aware Multimedia Pricing Model using Game Theory and Prospect Theory for Wireless Communications Krishna Murthy Kattiyan RamamoorthySan Diego State University | |||||||||||||||||||||||||||||||||||||||||
11:40 - 11:55 | API Design Implications of Boilerplate Client Code Daye NamCarnegie Mellon University | |||||||||||||||||||||||||||||||||||||||||
11:55 - 12:10 | Compile-time detection of machine image sniping Martin KelloggUniversity of Washington, Seattle |