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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wang, Ziyu Yang, Jiahai Li, Fuliang |
| Copyright Year | 2014 |
| Abstract | Anomaly detection has been a hot topic in recent years due to its capability of detecting zero day attacks. In this paper, we propose a new metric called Entropy-Ratio. We validate that the Entropy-Ratio is stationary. Making use of this observation, we combine the Least Mean Square algorithm and the Forward Linear Predictor to propose a new on-line detector called LMS-FLP detector. Using the two synthetic data sets - CEGI-6IX synthetic data and CERNET2 synthetic data, we validate that the LMS-FLP detector is very effective in detecting both anomalies involving many small IP flows and anomalies involving a few large IP flows. |
| Starting Page | 90 |
| Ending Page | 97 |
| File Size | 357739 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781479965137 |
| DOI | 10.1109/TrustCom.2014.16 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-09-24 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Forward Linear Predictor Detectors Entropy-Ratio Educational institutions Vectors Entropy IP networks Mathematical model Equations Anomaly detection Least Mean Square |
| Content Type | Text |
| Resource Type | Article |
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