Abstract
Internet traffic is continuously growing fast due to the rapid spread of the internet and the speed-up of the internet connection. Also, the applications provided on the internet have become more diversified. To support the QoS requirements for these internet applications, it would be better to measure the traffic volume according to the applications. Therefore, we are engaged in the application classification method, which is an offline technique for identifying the applications in units of flow. In some application classification methods, the applications of the target flows are analyzed according to their statistics on traffic metric, or features. We focus on these feature based classification methods, since the methods have the advantage that the port number and the packet payload need not be checked for classification. In the field of the machine learning, the classification methods that consist of multiple classifiers have been discussed. This is why the classification methods are improved in performance. However, the conventional feature based classification methods consists of single classifier. Also, the design of multiple classifiers has hardly been discussed. The design includes the way of combining some classifiers. Here, we introduce the fusion of multiple classifiers and propose applying the score level fusion using feature vectors to concatenate each classifier score to classify the flow into applications.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010 |
| Pages | 105-110 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2010 |
| Event | 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010 - Yamagata Duration: 2010 Aug 18 → 2010 Aug 20 |
Other
| Other | 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010 |
|---|---|
| City | Yamagata |
| Period | 10/8/18 → 10/8/20 |
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Keywords
- Application classification
- Internet traffic
- Multiple classifier
- Score level fusion
ASJC Scopus subject areas
- Information Systems
Cite this
Internet traffic classification using score level fusion of multiple classifier. / Ichino, Masatsugu; Maeda, Hiroaki; Yamashita, Takeshi; Hoshi, Kentaro; Komatsu, Naohisa; Takeshita, Kei; Tsujino, Masayuki; Iwashita, Motoi; Yoshino, Hideaki.
Proceedings - 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010. 2010. p. 105-110 5593127.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Internet traffic classification using score level fusion of multiple classifier
AU - Ichino, Masatsugu
AU - Maeda, Hiroaki
AU - Yamashita, Takeshi
AU - Hoshi, Kentaro
AU - Komatsu, Naohisa
AU - Takeshita, Kei
AU - Tsujino, Masayuki
AU - Iwashita, Motoi
AU - Yoshino, Hideaki
PY - 2010
Y1 - 2010
N2 - Internet traffic is continuously growing fast due to the rapid spread of the internet and the speed-up of the internet connection. Also, the applications provided on the internet have become more diversified. To support the QoS requirements for these internet applications, it would be better to measure the traffic volume according to the applications. Therefore, we are engaged in the application classification method, which is an offline technique for identifying the applications in units of flow. In some application classification methods, the applications of the target flows are analyzed according to their statistics on traffic metric, or features. We focus on these feature based classification methods, since the methods have the advantage that the port number and the packet payload need not be checked for classification. In the field of the machine learning, the classification methods that consist of multiple classifiers have been discussed. This is why the classification methods are improved in performance. However, the conventional feature based classification methods consists of single classifier. Also, the design of multiple classifiers has hardly been discussed. The design includes the way of combining some classifiers. Here, we introduce the fusion of multiple classifiers and propose applying the score level fusion using feature vectors to concatenate each classifier score to classify the flow into applications.
AB - Internet traffic is continuously growing fast due to the rapid spread of the internet and the speed-up of the internet connection. Also, the applications provided on the internet have become more diversified. To support the QoS requirements for these internet applications, it would be better to measure the traffic volume according to the applications. Therefore, we are engaged in the application classification method, which is an offline technique for identifying the applications in units of flow. In some application classification methods, the applications of the target flows are analyzed according to their statistics on traffic metric, or features. We focus on these feature based classification methods, since the methods have the advantage that the port number and the packet payload need not be checked for classification. In the field of the machine learning, the classification methods that consist of multiple classifiers have been discussed. This is why the classification methods are improved in performance. However, the conventional feature based classification methods consists of single classifier. Also, the design of multiple classifiers has hardly been discussed. The design includes the way of combining some classifiers. Here, we introduce the fusion of multiple classifiers and propose applying the score level fusion using feature vectors to concatenate each classifier score to classify the flow into applications.
KW - Application classification
KW - Internet traffic
KW - Multiple classifier
KW - Score level fusion
UR - http://www.scopus.com/inward/record.url?scp=78649258064&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78649258064&partnerID=8YFLogxK
U2 - 10.1109/ICIS.2010.48
DO - 10.1109/ICIS.2010.48
M3 - Conference contribution
SN - 9780769541471
SP - 105
EP - 110
BT - Proceedings - 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010
ER -