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Droid-Sec: deep learning in android malware detection
Open accessAbstract
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.
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Cited by
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Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2844341 · Cited by 1,471
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Berman, D. S., Buczak, A. L., Chavis, J. S., & Corbett, C. L. (2019) · Information · doi:10.3390/info10040122 · Cited by 569
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Metadata
- Authors
- Yuan, Z., Lu, Y., Wang, Z., & Xue, Y.
- Venue
- Proceedings of the 2014 ACM conference on SIGCOMM
- Publisher
- ACM
- Published
- 2014-08-17
- Pages
- 371-372
- OpenAlex
- W2110889728
- Cited by
- 371
- References
- 0
- FWCI
- 10.91
- Type
- conference paper
- Language
- en
- Open access
- gold
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- Yuan, Z., Lu, Y., Wang, Z., & Xue, Y. (2014). Droid-Sec: deep learning in android malware detection. Proceedings of the 2014 ACM conference on SIGCOMM. https://doi.org/10.1145/2619239.2631434
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