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): Jin L.C. Guo, Jie M. Zhang
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 NovDisplayed time zone: Tijuana, Baja California change
Wed 13 NovDisplayed time zone: Tijuana, Baja California change
10:40 - 12:20 | Student Research Competition - Selected Presentations (Graduate)Student Research Competition at South Park Chair(s): Jin L.C. Guo McGill University, Jie M. Zhang University College London, UK | ||
10:40 15m | Toward Practical Automatic Program Repair Student Research Competition Ali Ghanbari Iowa State University | ||
10:55 15m | Verifying Determinism in Sequential Programs Student Research Competition Rashmi Mudduluru University of Washington, Seattle | ||
11:10 15m | An Image-inspired and CNN-based Android Malware Detection Approach Student Research Competition Shao Yang Case Western Reserve University | ||
11:25 15m | User Preference Aware Multimedia Pricing Model using Game Theory and Prospect Theory for Wireless Communications Student Research Competition Krishna Murthy Kattiyan Ramamoorthy San Diego State University | ||
11:40 15m | API Design Implications of Boilerplate Client Code Student Research Competition Daye Nam Carnegie Mellon University | ||
11:55 15m | Compile-time detection of machine image sniping Student Research Competition Martin Kellogg University of Washington, Seattle |