A kernel-based nonparametric test for anomaly detection over line networks

@article{Zou2014AKN,
  title={A kernel-based nonparametric test for anomaly detection over line networks},
  author={Shaofeng Zou and Yingbin Liang and H. Vincent Poor},
  journal={2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)},
  year={2014},
  pages={1-6}
}
  • Shaofeng Zou, Yingbin Liang, H. Vincent Poor
  • Published in
    IEEE International Workshop…
    2014
  • Mathematics, Computer Science
  • The nonparametric problem of detecting existence of an anomalous interval over a one-dimensional line network is studied. Nodes corresponding to an anomalous interval (if one exists) receive samples generated by a distribution q, which is different from the distribution p that generates samples for other nodes. If an anomalous interval does not exist, then all nodes receive samples generated by p. It is assumed that the distributions p and q are arbitrary, and are unknown. In order to detect… CONTINUE READING

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