Hierarchical Real-time Network Traffic Classification Based on ECOC
Classification of network traffic is basic and essential for manynetwork researches and managements. With the rapid development ofpeer-to-peer (P2P) application using dynamic port disguisingtechniques and encryption to avoid detection, port-based and simplepayload-based network traffic classificatio...
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| Main Authors: | , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2014-02-01.
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| Online Access: | Get fulltext |
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| LEADER | 02626 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | ijeecs3162_1278 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Zhao, Yaou |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Xie, Xiao |e author |
| 700 | 1 | 0 | |a Jiang, Mingyan |e author |
| 245 | 0 | 0 | |a Hierarchical Real-time Network Traffic Classification Based on ECOC |
| 260 | |b Institute of Advanced Engineering and Science, |c 2014-02-01. | ||
| 520 | |a Classification of network traffic is basic and essential for manynetwork researches and managements. With the rapid development ofpeer-to-peer (P2P) application using dynamic port disguisingtechniques and encryption to avoid detection, port-based and simplepayload-based network traffic classification methods were diminished.An alternative method based on statistics and machine learning hadattracted researchers' attention in recent years. However, most ofthe proposed algorithms were off-line and usually used a single classifier.In this paper a new hierarchical real-time model was proposed which comprised of a three tuple (source ip, destination ip and destination port)look up table(TT-LUT) part and layered milestone part. TT-LUT was used to quickly classify short flows whichneed not to pass the layered milestone part, and milestones in layered milestone partcould classify the other flows in real-time with the real-time feature selection and statistics.Every milestone was a ECOC(Error-Correcting Output Codes) based model which was usedto improve classification performance. Experiments showed that the proposedmodel can improve the efficiency of real-time to 80%, and themulti-class classification accuracy encouragingly to 91.4% on the datasets which had been captured from the backbone router in our campus through a week. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.3877 | ||
| 540 | |a Copyright (c) 2014 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc-nd/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Computer Networks; Computational Intelligence | ||
| 690 | |a Hierarchical Real-time Model; Network Traffic Classification; ECOC | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 12, No 2: February 2014; 1551-1560 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v12.i2 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/3162/1278 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/3162/1278 |z Get fulltext |