Name
Abstract | ||
|---|---|---|
Spam e-mails, known as unsolicited e-mail messages, have become an increasing problem for information security. The intrusion of spam e-mails persecute the users and waste the network resources. Traditionally, machine learning and statistical filtering systems are used to filter out spam e-mails. However, there is no unique method can be successfully applied to classify spam e-mails. It is necessary to apply multiple approaches to detect spam and effectively filter out the increasing volumes of spam e-mails. In this paper, an ensemble approach, based on decision tree, support vector machine and back-propagation network, is applied to classify spam e-mails. The proposed approach is based on the characteristics of the spam e-mails. The spam e-mails are categorized into 14 features and then the ensemble approach is performed to classify them. From simulation results, the proposed ensemble approach outperforms other approaches for two test datasets. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.eswa.2009.07.080 | Expert Syst. Appl. |
Keywords | Field | DocType |
support vector machine,multiple approach,back-propagation network,proposed ensemble approach,e-mail,increasing problem,spam e-mail,network resource,decision tree,ensemble,ensemble approach,spam,machine learning,information security | Bag-of-words model,Decision tree,Data mining,Resource (disambiguation),Intrusion,Computer science,Support vector machine,Information security,Filter (signal processing),Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
37 | 3 | Expert Systems With Applications |
Citations | PageRank | References |
5 | 0.70 | 20 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kuo-Ching Ying | 1 | 712 | 36.47 |
| Shih-Wei Lin | 2 | 1059 | 46.26 |
| Zne-Jung Lee | 3 | 940 | 43.45 |
| Yen-Tim Lin | 4 | 5 | 0.70 |