The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Deep Ground Truth Analysis of Current Android Malware
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Dissecting Android Malware: Characterization and Evolution
Zhou, Y., & Jiang, X. (2012) · 2012 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2012.16 · Cited by 2,133
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
Wei, F., Roy, S., Ou, X., & Robby (2014) · Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2660267.2660357 · Cited by 415
Self-citationCopperDroid: Automatic Reconstruction of Android Malware Behaviors
Tam, K., Khan, S. J., Fattori, A., & Cavallaro, L. (2015) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2015.23145 · Cited by 398
AVclass: A Tool for Massive Malware Labeling
Sebastián, M., Rivera, R., Kotzias, P., & Caballero, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-45719-2_11 · Cited by 385
ANDRUBIS -- 1,000,000 Apps Later: A View on Current Android Malware Behaviors
Lindorfer, M., Neugschwandtner, M., Weichselbaum, L., Fratantonio, Y., Veen, V. V. D., & Platzer, C. (2014) · 2014 Third International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS) · doi:10.1109/badgers.2014.7 · Cited by 304
Finding unknown malice in 10 seconds: mass vetting for new threats at the Google-play scale
Chen, K., Wang, P., Lee, Y., Wang, X., Zhang, N., Huang, H., Zou, W., & Liu, P. (2015) · USENIX Security Symposium · Cited by 160
Harvesting Runtime Values in Android Applications That Feature Anti-Analysis Techniques
Rasthofer, S., Arzt, S., Miltenberger, M., & Bodden, E. (2016) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2016.23066 · Cited by 148
What the App is That? Deception and Countermeasures in the Android User Interface
Bianchi, A., Corbetta, J., Invernizzi, L., Fratantonio, Y., Kruegel, C., & Vigna, G. (2015) · 2015 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2015.62 · Cited by 147 · cited 1×
“Researchers have identified ways in which Android users can be deceived to misidentify a malicious app window as a legitimate app’s [12].”
Background
Cited by
graph2vec: Learning Distributed Representations of Graphs
Narayanan, A. S., Chandramohan, M., Venkatesan, R., Chen, L., Liu, Y., & Jaiswal, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.05005 · Cited by 494
A Survey on Ransomware: Evolution, Taxonomy, and Defense Solutions
Oz, H., Aris, A., Levi, A., & Uluagac, A. S. (2022) · ACM Computing Surveys · doi:10.1145/3514229 · Cited by 297
Metadata
- Authors
- Wei, F., Li, Y., Roy, S., Ou, X., & Zhou, W.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 252-276
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2620844046
- Cited by
- 398
- References
- 11
- FWCI
- 34.66
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Deep Ground Truth Analysis of Current Android Malware. https://app.cochat.ai/papers/10.1007/978-3-319-60876-1_12
- The paper
- Wei, F., Li, Y., Roy, S., Ou, X., & Zhou, W. (2017). Deep Ground Truth Analysis of Current Android Malware. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-60876-1_12
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.
