The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
MalDozer: Automatic framework for android malware detection using deep learning
Open accessAbstract
Android OS experiences a blazing popularity since the last few years. This predominant platform has established itself not only in the mobile world but also in the Internet of Things (IoT) devices. This popularity, however, comes at the expense of security, as it has become a tempting target of malicious apps. Hence, there is an increasing need for sophisticated, automatic, and portable malware detection solutions. In this paper, we propose MalDozer, an automatic Android malware detection and family attribution framework that relies on sequences classification using deep learning techniques. Starting from the raw sequence of the app's API method calls, MalDozer automatically extracts and learns the malicious and the benign patterns from the actual samples to detect Android malware. MalDozer can serve as a ubiquitous malware detection system that is not only deployed on servers, but also on mobile and even IoT devices. We evaluate MalDozer on multiple Android malware datasets ranging from 1 K to 33 K malware apps, and 38 K benign apps. The results show that MalDozer can correctly detect malware and attribute them to their actual families with an F1-Score of 96%–99% and a false positive rate of 0.06%–2%, under all tested datasets and settings.
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Cites
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
(2016) · Cited by 11,679
Drebin: Effective and Explainable Detection of Android Malware in Your Pocket
Arp, D., Spreitzenbarth, M., Hübner, M., Gascon, H., & Rieck, K. (2014) · Proceedings 2014 Network and Distributed System Security Symposium · doi:10.14722/ndss.2014.23247 · Cited by 2,331
AndroZoo: collecting millions of Android apps for the research community
Allix, K., Bissyandé, T. F., Klein, J., & Le Traon, Y. (2016) · Proceedings of the 13th International Conference on Mining Software Repositories · doi:10.1145/2901739.2903508 · Cited by 825
Hey, You, Get Off of My Market: Detecting Malicious Apps in Official and Alternative Android Markets
Zhou, Y., Wang, Z., Zhou, W., & Jiang, X. (2012) · Network and Distributed System Security Symposium · Cited by 734
RiskRanker: scalable and accurate zero-day android malware detection
Grace, M., Zhou, Y., Zhang, Q., Zou, S., & Jiang, X. (2012) · Proceedings of the 10th international conference on Mobile systems, applications, and services · doi:10.1145/2307636.2307663 · Cited by 595
Industry 4.0: The Industrial Internet of Things
Gilchrist, A. (2016) · Cited by 472
Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs
Zhang, M., Duan, Y., Yin, H., & Zhao, Z. (2014) · Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2660267.2660359 · Cited by 453
Cited by
Deep Learning for Anomaly Detection: A Survey
Chalapathy, R., & Chawla, S. (2019) · arXiv · doi:10.48550/arxiv.1901.03407 · Cited by 1,240
Sarker, I. H., Khan, A. I., Abushark, Y. B., & Alsolami, F. (2023) · Mobile Networks and Applications · doi:10.1007/s11036-022-01937-3 · Cited by 398
Onwuzurike, L., Mariconti, E., Andriotis, P., Cristofaro, E. D., Ross, G., & Stringhini, G. (2019) · ACM Transactions on Privacy and Security · doi:10.1145/3313391 · Cited by 334
Application of deep learning to cybersecurity: A survey
Mahdavifar, S., & Ghorbani, A. A. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.02.056 · Cited by 325
Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective
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Metadata
- Authors
- Karbab, E. B., Debbabi, M., Derhab, A., & Mouheb, D.
- Venue
- Digital Investigation
- Publisher
- Elsevier BV
- Published
- 2018-03
- Volume
- 24
- Pages
- S48-S59
- ISSN
- 1742-2876 (ISSN-L)
- OpenAlex
- W2789983203
- Cited by
- 403
- References
- 24
- FWCI
- 34.84
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Karbab, E. B., Debbabi, M., Derhab, A., & Mouheb, D. (2018). MalDozer: Automatic framework for android malware detection using deep learning. Digital Investigation. https://doi.org/10.1016/j.diin.2018.01.007
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