The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Crowdroid: behavior-based malware detection system for Android
Abstract
The sharp increase in the number of smartphones on the market, with the Android platform posed to becoming a market leader makes the need for malware analysis on this platform an urgent issue.
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Citations
Cites
(1967) · Cited by 23,719
TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones
Enck, W., Gilbert, P., Chun, B.-G., Cox, L. P., Jung, J., McDaniel, P. D., & Sheth, A. N. (2010) · doi:10.5555/1924943.1924971 · Cited by 2,238
Intrusion detection using sequences of system calls
Hofmeyr, S. A., Forrest, S., & Somayaji, A. (1998) · Journal of Computer Security · doi:10.3233/jcs-980109 · Cited by 1,313
A study of android application security
Enck, W., Octeau, D., McDaniel, P. D., & Chaudhuri, S. (2011) · Cited by 871
A survey on automated dynamic malware-analysis techniques and tools
Egele, M., Scholte, T., Kirda, E., & Kruegel, C. (2012) · ACM Computing Surveys · doi:10.1145/2089125.2089126 · Cited by 871
Limits of Static Analysis for Malware Detection
Moser, A., Kruegel, C., & Kirda, E. (2007) · Twenty-Third Annual Computer Security Applications Conference (ACSAC 2007) · doi:10.1109/acsac.2007.21 · Cited by 813
“Andromaly”: a behavioral malware detection framework for android devices
Shabtai, A., Kanonov, U., Elovici, Y., Glezer, C., & Weiss, Y. (2012) · Journal of Intelligent Information Systems · doi:10.1007/s10844-010-0148-x · Cited by 754
Static Analysis of Executables to Detect Malicious Patterns
Christodorescu, M., & Jha, S. (2006) · doi:10.21236/ada449067 · Cited by 600
Learning and Classification of Malware Behavior
Rieck, K., Holz, T., Willems, C., Düssel, P., & Laskov, P. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-70542-0_6 · Cited by 589
Understanding Android Security
Enck, W., Ongtang, M., & McDaniel, P. (2009) · IEEE Security & Privacy Magazine · doi:10.1109/msp.2009.26 · Cited by 544
Cited by
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
DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android
Aafer, Y., Du, W., & Yin, H. (2013) · Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-04283-1_6 · Cited by 686
A Comprehensive Review on Malware Detection Approaches
Aslan, O., & Samet, R. (2020) · IEEE Access · doi:10.1109/access.2019.2963724 · Cited by 649
Android Security: A Survey of Issues, Malware Penetration, and Defenses
Faruki, P., Bharmal, A., Laxmi, V., Ganmoor, V., Gaur, M. S., Conti, M., & Rajarajan, M. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2386139 · Cited by 559
A Survey on Security for Mobile Devices
La Polla, M., Martinelli, F., & Sgandurra, D. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2012.013012.00028 · Cited by 512
Metadata
- Authors
- Burguera, I., Zurutuza, U., & Nadjm-Tehrani, S.
- Venue
- Proceedings of the 1st ACM workshop on Security and privacy in smartphones and mobile devices
- Publisher
- ACM
- Published
- 2011-10-17
- Pages
- 15-26
- OpenAlex
- W1990649188
- Cited by
- 910
- References
- 26
- FWCI
- 72.28
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Crowdroid: behavior-based malware detection system for Android. https://app.cochat.ai/papers/10.1145/2046614.2046619
- The paper
- Burguera, I., Zurutuza, U., & Nadjm-Tehrani, S. (2011). Crowdroid: behavior-based malware detection system for Android. Proceedings of the 1st ACM workshop on Security and privacy in smartphones and mobile devices. https://doi.org/10.1145/2046614.2046619
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
