SAMLDroid: A Static Taint Analysis and Machine Learning Combined High-Accuracy Method for Identifying Android Apps with Location Privacy Leakage Risks.
- DOI
- 10.3390/e23111489
- Published
- 2021 Nov 10
- Container
- Entropy (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.3390/e23111489,
title = {SAMLDroid: A Static Taint Analysis and Machine Learning Combined High-Accuracy Method for Identifying Android Apps with Location Privacy Leakage Risks.},
author = {Hu G and Zhang B and Xiao X and Zhang W and Liao L and Zhou Y and Yan X},
year = {2021},
journal = {Entropy (Basel, Switzerland)},
doi = {10.3390/e23111489},
url = {https://doi.org/10.3390/e23111489}
}RIS
TY - JOUR TI - SAMLDroid: A Static Taint Analysis and Machine Learning Combined High-Accuracy Method for Identifying Android Apps with Location Privacy Leakage Risks. AU - Hu G AU - Zhang B AU - Xiao X AU - Zhang W AU - Liao L AU - Zhou Y AU - Yan X PY - 2021 JO - Entropy (Basel, Switzerland) DO - 10.3390/e23111489 UR - https://doi.org/10.3390/e23111489 ER -
APA
G, H., B, Z., X, X., W, Z., L, L., Y, Z., & X, Y. (2021). SAMLDroid: A Static Taint Analysis and Machine Learning Combined High-Accuracy Method for Identifying Android Apps with Location Privacy Leakage Risks.. Entropy (Basel, Switzerland). https://doi.org/10.3390/e23111489
Source records
- pubmed · retrieved 2026-09-25T09:17:56.908Z