DL-Droid: Deep learning based android malware detection using real devices

The Android operating system has been the most popular for smartphones and tablets since 2012. This popularity has led to a rapid raise of Android malware in recent years. The sophistication of Android malware obfuscation and detection avoidance methods have significantly improved, making many tradi...

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Bibliographic Details
Published in:Computers & security Vol. 89; p. 101663
Main Authors: Alzaylaee, Mohammed K., Yerima, Suleiman Y., Sezer, Sakir
Format: Journal Article
Language:English
Published: Amsterdam Elsevier Ltd 01.02.2020
Elsevier Sequoia S.A
Subjects:
Android
Code coverage
Deep learning
Dynamic analysis
Experiments
Machine learning
Malware
Malware detection
Mobile operating systems
Mobile security
Popularity
Smartphones
Sophistication
Static analysis
Tablet computers
Deep learning
Dynamic analysis
Machine learning
Static analysis
Mobile security
Code coverage
Malware detection
Android
ISSN:0167-4048, 1872-6208
Online Access:Get full text
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Summary:The Android operating system has been the most popular for smartphones and tablets since 2012. This popularity has led to a rapid raise of Android malware in recent years. The sophistication of Android malware obfuscation and detection avoidance methods have significantly improved, making many traditional malware detection methods obsolete. In this paper, we propose DL-Droid, a deep learning system to detect malicious Android applications through dynamic analysis using stateful input generation. Experiments performed with over 30,000 applications (benign and malware) on real devices are presented. Furthermore, experiments were also conducted to compare the detection performance and code coverage of the stateful input generation method with the commonly used stateless approach using the deep learning system. Our study reveals that DL-Droid can achieve up to 97.8% detection rate (with dynamic features only) and 99.6% detection rate (with dynamic + static features) respectively which outperforms traditional machine learning techniques. Furthermore, the results highlight the significance of enhanced input generation for dynamic analysis as DL-Droid with the state-based input generation is shown to outperform the existing state-of-the-art approaches.
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ISSN:0167-4048
1872-6208
DOI:10.1016/j.cose.2019.101663