Symbiotic filtering for spam email detection

• Main Author: Lopes, Clotilde
• Publication Date: 2011
• Other Authors: Cortez, Paulo, Sousa, Pedro, Rocha, Miguel, Rio, Miguel
• Format: Article
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: https://hdl.handle.net/1822/12042
• topic: Anti-spam filtering; Naive bayes; Collaborative filtering; Content-based filtering; Word attacks; Science & Technology

This paper presents a novel spam filtering technique called Symbiotic Filtering (SF) that aggregates distinct local filters from several users to improve the overall perfor- mance 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 con- tamination attacks. Moreover, it can be implemented and deployed with few effort and low communication costs, while assuring privacy.