Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.eswa.2011.01.174 | Expert Syst. Appl. |
Keywords | Field | DocType |
collaborative filtering,contamination attack,spam email detection,overall performance,aggregates distinct local filter,anti-spam filtering,novel spam,content-based filtering,hybrid approach,incremental symbiotic group,word attacks,symbiotic filtering,naive bayes cbf algorithm,spam detection,naive bayes,social network | Data mining,Social network,Collaborative filtering,Naive Bayes classifier,Computer science,Filter (signal processing),Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
38 | 8 | Expert Systems With Applications |
Citations | PageRank | References |
15 | 0.64 | 22 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Clotilde Lopes | 1 | 15 | 0.64 |
| Paulo Cortez | 2 | 157 | 12.29 |
| Pedro Sousa | 3 | 174 | 25.25 |
| Miguel Rocha | 4 | 511 | 54.06 |
| Miguel Rio | 5 | 277 | 29.40 |