지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2013.4
- 수록면
- 112 - 122 (11page)
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초록· 키워드
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Internet traffic classification is important for network traffic engineering and management. Previous studies have shown that machine learning-based traffic classification methods can obtain high classification accuracy in a static classification context. On the other hand, Internet traffic flows are dynamic, and the classification model needs to be updated at certain intervals. This study first examined the concept drift situation in multi class Internet traffic classification. The classifier obtains high classification accuracy for the majority class over a long period, but looses the classification accuracy for the minority class within a short time. This suggests that concept drift occurs easily in the minority class. To adapt the dynamic classification context, this paper proposes a classification framework based on the concept drift detection method. The experiment results on real traffic datasets showed that the proposed approach can promptly detect concept drift for each class and improve the recall of the minority class while maintaining high overall accuracy.
목차
Abstract
Introduction
Internet traffic datasets and concept drift
Classification framework based PCDD
Experiments
Conclusions
References
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UCI(KEPA) : I410-ECN-0101-2015-500-002466428