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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | LiTing Hu LiJun Zhang |
| Copyright Year | 2012 |
| Description | Author affiliation: School of Computer Science and Engineering, BeiHang University, Beijing, China (LiTing Hu; LiJun Zhang) |
| Abstract | Real-time Internet traffic identification is always a hot research topic in recent years. It involves quality of service, network accounting, Intrusion Detection and so on. Traditional identification approaches, such as those based on port and payload analysis, are no longer applicable in actual networks. In this paper we present a machine-learning approach, independent of port numbers, to accurately classify Internet traffic using decision tree. In our work, we think over not only the accuracy, but also the time cost. We use FCBF to remove redundant features and C4.5 algorithm to build the classification model and guarantee both accuracy and efficiency. |
| Starting Page | 1 |
| Ending Page | 3 |
| File Size | 226086 |
| Page Count | 3 |
| File Format | |
| ISBN | 9781467344975 |
| ISSN | 21544824 |
| e-ISBN | 9781889334479 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-24 |
| Publisher Place | Mexico |
| Access Restriction | Subscribed |
| Rights Holder | TSI Press |
| Subject Keyword | Internet Accuracy Uncertainty Machine learning Decision trees Educational institutions Symmetrical uncertainty Internet traffic traffic classification Machine Learning |
| Content Type | Text |
| Resource Type | Article |
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