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Hybrid feature selection for phishing email detection
Phishing emails are more active than ever before and putting the average computer user and organizations at risk of significant data, brand and financial loss. Through an analysis of a number of phishing and ham email collected, this paper focused on fundamental attacker behavior which could be extracted from email header. It also put forward a hybrid feature selection approach based on combination of content-based and behavior-based. The approach could mine the attacker behavior based on email header. On a publicly available test corpus, our hybrid features selections are able to achieve 96% accuracy rate. In addition, we successfully tested the quality of our proposed behavior-based feature using the information gain.
History
Title of book
Algorithms and architectures for parallel processingSeries
Lecture notes in computer science; v. 7017Chapter number
26Pagination
266 - 275Publisher
SpringerPlace of publication
Berlin, GermanyISSN
0302-9743eISSN
1611-3349ISBN-13
9783642246494Language
engPublication classification
B1 Book chapterCopyright notice
2011, Springer-Verlag BerlinExtent
38Editor/Contributor(s)
Y Xiang, A Cuzzocrea, M Hobbs, W ZhouUsage metrics
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