Jeffrey Erman
Carey Williamson
- O. Chapelle, B. Schölkopf, and A. Zien, editors. Semi-Supervised Learning. MIT Press, Cambridge, MA, 2006.Google Scholar
Digital Library
- R. Duda, P. Hart, and D. Stork. Pattern Classification. Wiley, second edition, 2001. Google ScholarDigital Library
- J. Erman, M. Arlitt, and A. Mahanti. Traffic Classification using Clustering Algorithms. In SIGCOMM'06 MineNet Workshop, Pisa, Italy, September 2006. Google ScholarDigital Library
- J. Erman, A. Mahanti, M. Arlitt, I. Cohen, and C. Williamson. Offline/Online Traffic Classification Using Semi-Supervised Learning. Technical report, University of Calgary, 2007.Google Scholar
- J. Erman, A. Mahanti, M. Arlitt, and C. Williamson. Identifying and Discriminating Between Web and Peer-to-Peer traffic in the Network Core. In WWW'07, Banff, Canada, May 2007. Google ScholarDigital Library
- V. Paxson. Bro: A System for Detecting Network Intruders in Real-Time. Comput. Networks, 31(23-24):2435--2463, 1999. Google ScholarDigital Library
Index Terms
- Semi-supervised network traffic classification
Recommendations
Semi-supervised and Compound Classification of Network Traffic
ICDCSW '12: Proceedings of the 2012 32nd International Conference on Distributed Computing Systems WorkshopsThis paper presents a new semi-supervised method to effectively improve traffic classification performance when few supervised training data are available. Existing semi supervised methods label a large proportion of testing flows as unknown flows due ...
Semi-supervised boosting for multi-class classification
ECMLPKDD'08: Proceedings of the 2008th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part IIMost semi-supervised learning algorithms have been designed for binary classification, and are extended to multi-class classification by approaches such as one-against-the-rest. The main shortcoming of these approaches is that they are unable to exploit ...
Semi-supervised multi-class Adaboost by exploiting unlabeled data
Research highlights We propose a semi-supervised learning method by using the multi-class boosting. It handles K-class classification without reducing into multiple two-class problems. The classification accuracy of base classifier requires only 1/K or ...
Comments