The DOI matches its Crossref record; the references it cites are not checked yet.
Device Administrator Use and Abuse in Android: Detection and Characterization
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
The citation lists haven’t been fetched yet
Cited by 4
Research works printed but not linked: 13
- 10.1007/978-3-319-04283-1_6 · DOI · Paper page
- 10.1145/2901739.2903508 · DOI · Paper page
- Arp Daniel, 2014, DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket.. In NDSS.
- 10.1109/MOBILESoft.2017.2 · doi:10.1109/mobilesoft.2017.2
- 10.1145/2666356.2594299 · DOI · Paper page
- 10.1145/2462096.2462100 · DOI
- 10.1145/2635868.2635869 · DOI · Paper page
- 10.5555/2818754.2818791 · doi:10.5555/2818754.2818791
- Wei Fengguo, 2017, Deep Ground Truth Analysis of Current Android Malware. In International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA'17)
- 10.1145/2381934.2381950 · DOI · Paper page
- 10.1109/TrustCom.2013.25 · DOI · Paper page
- 10.1109/SP.2012.16 · DOI · Paper page
- 10.1109/SP.2012.16 · DOI · Paper page
Metadata
- Authors
- Shan, Z., Samuel, R., & Neamtiu, I.
- Venue
- The 25th Annual International Conference on Mobile Computing and Networking
- Publisher
- ACM
- Published
- 2019-10-11
- Pages
- 1-16
- Cited by
- 4
- Funders
- Army Research Laboratory (W911NF-13-2-0045)
- National Science Foundation (CNS-1617584)
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 ›
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Device Administrator Use and Abuse in Android: Detection and Characterization. https://app.cochat.ai/papers/10.1145/3300061.3345452
- The paper
- Shan, Z., Samuel, R., & Neamtiu, I. (2019). Device Administrator Use and Abuse in Android: Detection and Characterization. The 25th Annual International Conference on Mobile Computing and Networking. https://doi.org/10.1145/3300061.3345452
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.
