Name
Abstract | ||
|---|---|---|
Network anomaly detection is a vibrant research area. Researchers have approached this problem using various techniques such as artificial intelligence, machine learning, and state machine modeling. In this paper, we first review these anomaly detection methods and then describe in detail a statistical signal processing technique based on abrupt change detection. We show that this signal processing technique is effective at detecting several network anomalies. Case studies from real network data that demonstrate the power of the signal processing approach to network anomaly detection are presented. The application of signal processing techniques to this area is still in its infancy, and we believe that it has great potential to enhance the field, and thereby improve the reliability of IP networks. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/TSP.2003.814797 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
anomaly detection,network anomaly,network anomaly detection,signal processing approach,real network data,ip network,statistical signal processing technique,signal processing technique,anomaly detection method,abrupt change detection,state machine,machine learning,change detection,signal processing,statistical analysis,adaptive signal processing,artificial intelligent,statistical signal processing | Anomaly detection,Data mining,Signal processing,Change detection,Artificial intelligence,Adaptive filter,Mathematical optimization,Finite-state machine,Statistical signal processing,Reliability (computer networking),Mathematics,Machine learning,Network performance | Journal |
Volume | Issue | ISSN |
51 | 8 | 1053-587X |
Citations | PageRank | References |
134 | 8.33 | 26 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Thottan | 1 | 144 | 10.58 |
| Chuanyi Ji | 2 | 812 | 124.04 |