Towards misdirected email detection based on multi-attributes
Yiguo Pu, Jinqiao Shi, Xiaojun Chen, Li Jiang Guo, Tingwen Liu · 2015
Email has become widely used in recent years bringing with it new problems. Although this event doesn't happen often, misdirected emails can bring out great information leakage. It is not easy to detect these misdirected emails from legitimate ones since they may be only distinguishable in the sender's perspective. Existing methods discover misdirected emails from user agent or gateway but are not appropriate for varied application environment. This paper proposes a misdirected mail detection method based on multi-attributes which can be deployed on server side. Three type of attributes including email content fingerprinting, social relationship and meta information are considered in this method. Based on SVM classification algorithm, experiments show that it can detect misdirected emails with up to 91.6% accuracy.