PhishZoo: Detecting Phishing Websites by Looking at Them
By Sadia Afroz, Rachel Greenstadt. 2011
228 citations recorded by the source corpus.
Subfields: Information Systems, Sociology and Political Science, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Battista Biggio
- Fabio Roli
- Igino Corona
- Giorgio Fumera
- Davide Ariu
- Fadi A. Thabtah
- Steven C. H. Hoi
- Chenghao Liu
In Sociology and Political Science
- Battista Biggio
- Fadi A. Thabtah
- Fabio Roli
- Steven C. H. Hoi
- Chenghao Liu
- Neda Abdelhamid
- Aladdin Ayesh
- Doyen Sahoo
In Signal Processing
- Battista Biggio
- Fabio Roli
- Igino Corona
- Giorgio Fumera
- Giorgio Giacinto
- Doyen Sahoo
- Davide Ariu
- Davide Maiorca
Citations in the corpus
- DeltaPhish: Detecting Phishing Webpages in Compromised Websites (2017, background): “However, mainly due to the higher computational complexity of this kind of analysis, only few approaches have combined HTML and visual features for target-dependent phishing detection [11, 32], and it is not clear to which extent they can be robust against well-crafted adversarial attacks.”
- Phishing detection based Associative Classification data mining (2014, background): “One other solution that depends on the whitelist was presented in PhishZoo (Afroz & Greenstadt, 2011).”
- 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].”