The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Detecting Phishing Web Pages with Visual Similarity Assessment Based on Earth Mover's Distance (EMD)
Abstract
An effective approach to phishing Web page detection is proposed, which uses Earth mover's distance (EMD) to measure Web page visual similarity. We first convert the involved Web pages into low resolution images and then use color and coordinate features to represent the image signatures. We use EMD to calculate the signature distances of the images of the Web pages. We train an EMD threshold vector for classifying a Web page as a phishing or a normal one. Large-scale experiments with 10,281 suspected Web pages are carried out to show high classification precision, phishing recall, and applicable time performance for online enterprise solution. We also compare our method with two others to manifest its advantage. We also built up a real system which is already used online and it has caught many real phishing cases
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
A vector space model for automatic indexing
Salton, G., Wong, A., & Yang, C. S. (1975) · Communications of the ACM · doi:10.1145/361219.361220 · Cited by 7,521
The Earth Mover's Distance as a Metric for Image Retrieval
Rubner, Y., Tomasi, C., & Guibas, L. J. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026543900054 · Cited by 4,568
A metric for distributions with applications to image databases
Rubner, Y., Tomasi, C., & Guibas, L. J. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710701 · Cited by 1,726
The Distribution of a Product from Several Sources to Numerous Localities
Hitchcock, F. L. (1941) · Journal of Mathematics and Physics · doi:10.1002/sapm1941201224 · Cited by 1,609
Syntactic clustering of the Web
Broder, A. Z., Glassman, S. C., Manasse, M. S., & Zweig, G. (1997) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(97)00031-7 · Cited by 1,343
Russ, J. C. (2016) · CRC Press eBooks · doi:10.1201/b10720 · Cited by 1,022
The Earth Mover's distance is the Mallows distance: some insights from statistics
Levina, E., & Bickel, P. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937632 · Cited by 562
The battle against phishing: Dynamic Security Skins
Dhamija, R., & Tygar, J. D. (2005) · Proceedings of the 2005 symposium on Usable privacy and security - SOUPS '05 · doi:10.1145/1073001.1073009 · Cited by 442
Methods for identifying versioned and plagiarized documents
Hoad, T. C., & Zobel, J. (2003) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.10170 · Cited by 330
Detecting web page structure for adaptive viewing on small form factor devices
Chen, Y., Ma, W.-Y., & Zhang, H.-J. (2003) · Proceedings of the twelfth international conference on World Wide Web - WWW '03 · doi:10.1145/775152.775184 · Cited by 270
Cited by
Detecting Internet of Things attacks using distributed deep learning
De La Torre Parra, G., Rad, P., Choo, K.-K. R., & Beebe, N. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102662 · Cited by 328
A survey of phishing attack techniques, defence mechanisms and open research challenges
Jain, A. K., & Gupta, B. B. (2022) · Enterprise Information Systems · doi:10.1080/17517575.2021.1896786 · Cited by 192
Predicting Phishing Websites Using Classification Mining Techniques with Experimental Case Studies
Aburrous, M., Hossain, M. A., Dahal, K., & Thabtah, F. (2010) · 2010 Seventh International Conference on Information Technology: New Generations · doi:10.1109/itng.2010.117 · Cited by 86
Intelligent Phishing Website Detection System using Fuzzy Techniques
Aburrous, M., Hossain, M. A., Thabatah, F., & Dahal, K. (2008) · 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications · doi:10.1109/ictta.2008.4530019 · Cited by 68
Browser-Side Countermeasures for Deceptive Phishing Attack
Huang, H., Zhong, S., & Tan, J. (2009) · 2009 Fifth International Conference on Information Assurance and Security · doi:10.1109/ias.2009.12 · Cited by 31
Metadata
- Authors
- Fu, A. Y., Wenyin, L., & Deng, X.
- Venue
- IEEE Transactions on Dependable and Secure Computing
- Published
- 2006-10
- Volume
- 3
- Issue
- 4
- Pages
- 301-311
- ISSN
- 1545-5971 (ISSN-L)
- OpenAlex
- W2142995284
- Cited by
- 294
- References
- 17
- FWCI
- 20.94
- Type
- journal article
- 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: Detecting Phishing Web Pages with Visual Similarity Assessment Based on Earth Mover's Distance (EMD). https://app.cochat.ai/papers/10.1109/tdsc.2006.50
- The paper
- Fu, A. Y., Wenyin, L., & Deng, X. (2006). Detecting Phishing Web Pages with Visual Similarity Assessment Based on Earth Mover's Distance (EMD). IEEE Transactions on Dependable and Secure Computing. https://doi.org/10.1109/tdsc.2006.50
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.
