Droid-Sec: deep learning in android malware detection

As smartphones and mobile devices are rapidly becoming indispensable for many network users, mobile malware has become a serious threat in the network security and privacy. Especially on the popular Android platform, many malicious apps are hiding in a large number of normal apps, which makes the ma...

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Bibliographic Details
Main Authors: Yuan, Zhenlong, Lu, Yongqiang, Wang, Zhaoguo, Xue, Yibo
Format: Conference Proceeding
Language:English
Subjects:
Applications programs
Dynamic tests
Learning
Malware
Mobile communication systems
Networks
Platforms
Security and privacy > Network security
Smartphones
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Summary:As smartphones and mobile devices are rapidly becoming indispensable for many network users, mobile malware has become a serious threat in the network security and privacy. Especially on the popular Android platform, many malicious apps are hiding in a large number of normal apps, which makes the malware detection more challenging. In this paper, we propose a ML-based method that utilizes more than 200 features extracted from both static analysis and dynamic analysis of Android app for malware detection. The comparison of modeling results demonstrates that the deep learning technique is especially suitable for Android malware detection and can achieve a high level of 96% accuracy with real-world Android application sets.
ISSN:0146-4833
DOI:10.1145/2619239.2631434