Paper Info
Title
A hybrid phish detection approach by identity discovery and keywords retrieval
Abstract
Phishing is a significant security threat to the Internet, which causes tremendous economic loss every year. In this paper, we proposed a novel hybrid phish detection method based on information extraction (IE) and information retrieval (IR) techniques. The identity-based component of our method detects phishing webpages by directly discovering the inconsistency between their identity and the identity they are imitating. The keywords-retrieval component utilizes IR algorithms exploiting the power of search engines to identify phish. Our method requires no training data, no prior knowledge of phishing signatures and specific implementations, and thus is able to adapt quickly to constantly appearing new phishing patterns. Comprehensive experiments over a diverse spectrum of data sources with 11449 pages show that both components have a low false positive rate and the stacked approach achieves a true positive rate of 90.06% with a false positive rate of 1.95%.
Year
DOI
Venue
2009
10.1145/1526709.1526786
WWW
Keywords
Field
DocType
identity discovery,low false positive rate,identity-based component,information retrieval,keywords retrieval,hybrid phish detection approach,true positive rate,false positive rate,phishing signature,new phishing pattern,keywords-retrieval component,data source,information extraction,anti phishing,spectrum,search engine
False positive rate,Web page,Computer science,Tabnabbing,Artificial intelligence,The Internet,World Wide Web,Search engine,Phishing,Information retrieval,Information extraction,Named-entity recognition,Machine learning
Conference
Citations 
PageRank 
References 
57
2.19
8
Authors
2
Name
Order
Citations
PageRank
Guang Xiang138218.31
Jason Hong26706518.75