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Drebin: Effective and Explainable Detection of Android Malware in Your Pocket
Abstract
Malicious applications pose a threat to the security of the Android platform. The growing amount and diversity of these applications render conventional defenses largely ineffective and thus Android smartphones often remain un-protected from novel malware. In this paper, we propose DREBIN, a lightweight method for detection of Android malware that enables identifying malicious applications di-rectly on the smartphone. As the limited resources impede monitoring applications at run-time, DREBIN performs a broad static analysis, gathering as many features of an ap-plication as possible. These features are embedded in a joint vector space, such that typical patterns indicative for malware can be automatically identified and used for ex-plaining the decisions of our method. In an evaluation with 123,453 applications and 5,560 malware samples DREBIN outperforms several related approaches and detects 94% of the malware with few false alarms, where the explana-tions provided for each detection reveal relevant properties of the detected malware. On five popular smartphones, the method requires 10 seconds for an analysis on average, ren-dering it suitable for checking downloaded applications di-rectly on the device. 1
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Cites
Casting out Demons: Sanitizing Training Data for Anomaly Sensors
Cretu, G. F., Stavrou, A., Locasto, M. E., Stolfo, S. J., & Keromytis, A. D. (2008) · 2008 IEEE Symposium on Security and Privacy (sp 2008) · doi:10.1109/sp.2008.11 · Cited by 226
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Zhang, J. M., Harman, M., Ma, L., & Liu, Y. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2962027 · Cited by 897
A Survey of Deep Learning Methods for Cyber Security
Berman, D. S., Buczak, A. L., Chavis, J. S., & Corbett, C. L. (2019) · Information · doi:10.3390/info10040122 · Cited by 569
A Multimodal Deep Learning Method for Android Malware Detection Using Various Features
Kim, T., Kang, B., Rho, M., Sezer, S., & Im, E. G. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2866319 · Cited by 518
Explaining anomalies detected by autoencoders using Shapley Additive Explanations
Antwarg, L., Miller, R. M., Shapira, B., & Rokach, L. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115736 · Cited by 496
A Survey of App Store Analysis for Software Engineering
Martin, W., Sarro, F., Jia, Y., Zhang, Y., & Harman, M. (2017) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2630689 · Cited by 459
Metadata
- Authors
- Arp, D., Spreitzenbarth, M., Hübner, M., Gascon, H., & Rieck, K.
- Venue
- Proceedings 2014 Network and Distributed System Security Symposium
- Publisher
- Internet Society
- Published
- 2014
- OpenAlex
- W2122672392
- Cited by
- 2,331
- References
- 1
- FWCI
- 123.34
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Arp, D., Spreitzenbarth, M., Hübner, M., Gascon, H., & Rieck, K. (2014). Drebin: Effective and Explainable Detection of Android Malware in Your Pocket. Proceedings 2014 Network and Distributed System Security Symposium. https://doi.org/10.14722/ndss.2014.23247
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