Statistical Traffic Anomaly Detection in Time-Varying Communication Networks

@article{Wang2015StatisticalTA,
  title={Statistical Traffic Anomaly Detection in Time-Varying Communication Networks},
  author={Jing Wang and Ioannis Ch. Paschalidis},
  journal={IEEE Transactions on Control of Network Systems},
  year={2015},
  volume={2},
  pages={100-111}
}
We propose two methods for traffic anomaly detection in communication networks where properties of normal traffic evolve dynamically. We formulate the anomaly detection problem as a binary composite hypothesis testing problem and develop a model-free and a model-based method, leveraging techniques from the theory of large deviations. Both methods first extract a family of probability laws (PLs) that represent normal traffic patterns during different time-periods, and then detect anomalies by… CONTINUE READING
Highly Cited
This paper has 20 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 11 extracted citations

Anomaly Detection and Attribution in Networks With Temporally Correlated Traffic

IEEE/ACM Transactions on Networking • 2018
View 7 Excerpts
Highly Influenced

References

Publications referenced by this paper.
Showing 1-10 of 19 references

Statitical Anomaly Detector of Internet Traffic (SADIT),

J. Wang, J. Zhang, I. C. Paschalidis
https://github.com/hbhzwj/SADIT, • 2014
View 2 Excerpts

Anomaly detection techniques for data exfiltration attempts,

R. Locke, J. Wang, I. Paschalidis
Center for Information & Systems Engineering, Boston University, • 2012
View 1 Excerpt

Efficient network-wide flow record generation

2011 Proceedings IEEE INFOCOM • 2011
View 1 Excerpt

Similar Papers

Loading similar papers…