SOAP: A Social network Aided Personalized and effective spam filter to clean your e-mail box

Abstract

The explosive growth of unsolicited emails has prompted the development of numerous spam filtering techniques. A Bayesian spam filter is superior to a static keywordbased spam filter because it can continuously evolve to tackle new spam by learning keywords in new spam emails. However, Bayesian spam filters can be easily poisoned by avoiding spam keywords and adding many innocuous keywords in the emails. In addition, they need a significant amount of time to adapt to a new spam based on user feedback. Moreover, few current spam filters exploit social networks to assist spam detection. In order to develop an accurate and user-friendly spam filter, in this paper, we propose a SOcial network Aided Personalized and effective spam filter (SOAP). Unlike previous filters that focus on parsing keywords (e.g, Bayesian filter) or building blacklists, SOAP exploits the social relationship among email correspondents to detect the spam adaptively and automatically. SOAP integrates three components into the basic Bayesian filter: social closeness-based spam filtering, social interest-based spam filtering, and adaptive trust management. We evaluate performance of SOAP based on the trace data from Facebook. Experimental results show that SOAP can greatly improve the performance of Bayesian spam filters in terms of the accuracy, attack-resilience and efficiency of spam detection. We also find that the performance of Bayesian spam filters is the lower bound of SOAP.