Towards misdirected email detection for preventing information leakage

With the widespread usage of emails, information leakage via misdirected emails becomes a practical and disastrous problem, which should be addressed at all costs. Prior methods have two limitations: privacy issue as relying on email contents to work, and high cost as building too many targeted models. In this paper, we reduce the detection of misdirected emails to a binary classification problem, and build only a universal model to detect misdirected emails. We introduce some representative features that can vividly describe the characteristics of misdirected emails while not infringe users´ privacy. Then we design novel algorithms to get these features. The random forest classifier is chosen to perform the detecting task. Experimental results show that our work is able to detect misdirected emails with 89% precision rate and 82% recall rate in average.