Symbiotic filtering for spam email detection

This paper presents a novel spam filtering technique called Symbiotic Filtering (SF) that aggregates distinct local filters from several users to improve the overall performance of spam detection. SF is an hybrid approach combining some features from both Collaborative (CF) and Content-Based Filtering (CBF). It allows for the use of social networks to personalize and tailor the set of filters that serve as input to the filtering. A comparison is performed against the commonly used Naive Bayes CBF algorithm. Several experiments were held with the well-known Enron data, under both fixed and incremental symbiotic groups. We show that our system is competitive in performance and is robust against both dictionary and focused contamination attacks. Moreover, it can be implemented and deployed with few effort and low communication costs, while assuring privacy.

Research highlights
► A new Symbiotic Filtering (SF) approach is proposed for spam email detection.
► SF combines filters from distinct users while assuring privacy.
► SF outperformed local filtering for a small number of users (i.e. 3 to 5).
► SF is more robust to words attacks (e.g. dictionary, focused).