Lexical feature based phishing URL detection using online learning

Authors:
Aaron Blum;Brad Wardman;Thamar Solorio;Gary Warner
Affiliations:
The University of Alabama at Birmingham, Birmingham, AL, USA;The University of Alabama at Birmingham, Birmingham, AL, USA;The University of Alabama at Birmingham, Birmingham, AL, USA;The University of Alabama at Birmingham, Birmingham, AL, USA
Venue:
Proceedings of the 3rd ACM workshop on Artificial intelligence and security
Year:
2010

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Abstract

Phishing is a form of cybercrime where spammed emails and fraudulent websites entice victims to provide sensitive information to the phishers. The acquired sensitive information is subsequently used to steal identities or gain access to money. This paper explores the possibility of utilizing confidence weighted classification combined with content based phishing URL detection to produce a dynamic and extensible system for detection of present and emerging types of phishing domains. Our system is capable of detecting emerging threats as they appear and subsequently can provide increased protection against zero hour threats unlike traditional blacklisting techniques which function reactively.