Unsupervised Ensemble Anomaly Detection through Time-Periodical Packet Sampling

@article{Nawata2010UnsupervisedEA,
  title={Unsupervised Ensemble Anomaly Detection through Time-Periodical Packet Sampling},
  author={Shuichi Nawata and Masato Uchida and Yu Gu and Masato Tsuru and Yuji Oie},
  journal={2010 INFOCOM IEEE Conference on Computer Communications Workshops},
  year={2010},
  pages={1-6}
}
We propose an anomaly detection method that trains a baseline model describing the normal behavior of network traffic without using manually labeled traffic data. The trained baseline distribution is used as the basis for comparison with the audit network traffic. The proposed method can be carried out in an unsupervised manner through the use of time-periodical packet sampling for a different purpose from which it was intended. That is, we take advantage of the lossy nature of packet sampling… CONTINUE READING

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