Feature selection for Spam and Phishing detection

Unsolicited Bulk Email (UBE) has become a large problem in recent years. The number of mass mailers in existence is increasing dramatically. Automatically detecting UBE has become a vital area of current research. Many email clients (such as Outlook and Thunderbird) already have junk filters built in. Mass mailers are continually evolving and overcoming some of the junk filters. This means that the need for research in the area is ongoing. Many existing techniques seem to randomly choose the features that will be used for classification. This paper aims to address this issue by investigating the utility of over 40 features that have been used in recent literature. Information gain for these features are calculated over Ham, Spam and Phishing corpora.