Fighting Phishing with Discriminative Keypoint Features
By Kuan-Ta Chen, Jau-Yuan Chen, Chun-Rong Huang, Chu-Song Chen. 2009
115 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Battista Biggio
- Fabio Roli
- Doyen Sahoo
- Rachel Greenstadt
- Sadia Afroz
- Steven C. H. Hoi
- Chenghao Liu
- Giorgio Fumera
In Information Systems
- Battista Biggio
- Fabio Roli
- Igino Corona
- Steven C. H. Hoi
- Doyen Sahoo
- Chenghao Liu
- Giorgio Fumera
- Davide Ariu
Citations in the corpus
- DeltaPhish: Detecting Phishing Webpages in Compromised Websites (2017, background): “The main difference with target-independent approaches is that most of the target-dependent approaches have considered measures of visual similarity between webpage snapshots or embedded images, using a wide range of image analysis techniques, mostly based on computing lowlevel visual features, incl”
- PhishZoo: Detecting Phishing Websites by Looking at Them (2011, methodology): “used screenshot of webpages to detect phishing sites [22].”
- Malicious URL Detection using Machine Learning: A Survey (2017, methodology): “Contrast Context Histogram (CCH) features were suggested [59], and so were Scale Invariant Feature Transform (SIFT) features [60].”
- Phishing Detection: A Literature Survey (2013, background): “Fighting phishing with discriminative keypoint features of webpages [37] < 0.”