Coupled behavioral analysis for user preference-based email spamming

In this paper, we develop and implement a new email spamming system leveraged by coupled text similarity analysis on user preference and a virtual meta-layer user-based email network, we take the social networks or campus LAN networks as the spam social network scenario. Fewer current practices exploit social networking initiatives to assist in spam filtering. Social network has essentially a large number of accounts features and attributes to be considered. Instead of considering large amount of users accounts features, we construct a new model called meta-layer email network which can reduce these features by only considering individual user's actions as an indicator of user preference, these common user actions are considered to construct a social behavior-based email network. With the further analytic results from text similarity measurements for each individual email contents, the behavior-based virtual email network can be improved with much higher accuracy on user preferences. Further, a coupled selection model is developed for this email network, we are able to consider all relevant factors/features in a whole and recommend the emails practically to the user individually. The experimental results show the new approach can achieve higher precision and accuracy with better email ranking in favor of personalised preference.