The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Droiddetector: android malware characterization and detection using deep learning
Open accessAbstract
Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are hidden in a large number of benign apps in Android markets that seriously threaten Android security. Deep learning is a new area of machine learning research that has gained increasing attention in artificial intelligence. In this study, we propose to associate the features from the static analysis with features from dynamic analysis of Android apps and characterize malware using deep learning techniques. We implement an online deep-learning-based Android malware detection engine (DroidDetector) that can automatically detect whether an app is a malware or not. With thousands of Android apps, we thoroughly test DroidDetector and perform an indepth analysis on the features that deep learning essentially exploits to characterize malware. The results show that deep learning is suitable for characterizing Android malware and especially effective with the availability of more training data. DroidDetector can achieve 96.76% detection accuracy, which outperforms traditional machine learning techniques. An evaluation of ten popular anti-virus softwares demonstrates the urgency of advancing our capabilities in Android malware detection.
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Cites
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Metadata
- Authors
- Yuan, Z., Lu, Y., & Xue, Y.
- Venue
- Tsinghua Science and Technology
- Publisher
- Tsinghua University Press
- Published
- 2016-02
- Volume
- 21
- Issue
- 1
- Pages
- 114-123
- ISSN
- 1007-0214 (ISSN-L)
- OpenAlex
- W2313513770
- Cited by
- 462
- References
- 24
- FWCI
- 48.06
- Type
- journal article
- Language
- en
- Open access
- diamond
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