A Human Factors Approach to Spam Filtering
Robert Beverly · 2009
While misclassified spam imposes a burden on end-users, the cost of false positives is much higher. Therefore, significant effort has been spent attempting to conservatively optimize this binary classification decision. While modern email fil-tering is quite effective, the sheer volume of spam implies that even high precision and high recall filters yield non-zero misclassification, i.e. no classifier is “perfect ” against adapt-able adversaries. This paper makes explicit recognition of this balancing act and argues for: i) removing the burden of perfect classification from the classifier; ii) separating clas-sification and filtering tasks; and iii) a human factors ap-proach to filtering. We present initial work on SpamGUI, an operational and publicly available embodiment of these ideas. 1.