Name
Abstract | ||
|---|---|---|
Signature-driven spam detection provides an alternative to machine learning approaches and can be very effective when near-duplicates of essentially the same message are sent in high vol- ume (20). Unfortunately, signatures can also be brittle to small alterations of message content. In this work we propose a technique for increasing signature robustness, targeting the I-Match algorithm (6), but applicable to other single-signature detection schemes. The proposed method is shown to consis- tently outperform traditional I-Match in the spam filtering application. As I-Match signature quality and stability depend on vocabulary control, we compare the traditional Zipfian approaches to feature selection with techniques applied typically in text categorization, which are found to provide viable alternatives. In particular, distributional word clustering is demonstrated to be effective in increasing signature robustness. |
Year | Venue | Keywords |
|---|---|---|
2004 | CEAS | feature selection,machine learning |
Field | DocType | Citations |
Bag-of-words model,Data mining,World Wide Web,Feature selection,Pattern recognition,Computer science,Filter (signal processing),Robustness (computer science),Artificial intelligence,Text categorization,Cluster analysis,Vocabulary | Conference | 23 |
PageRank | References | Authors |
1.39 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aleksander Kołcz | 1 | 628 | 66.65 |
| Abdur Chowdhury | 2 | 2013 | 160.59 |
| Joshua Alspector | 3 | 445 | 267.78 |