Online anomaly detection in case of limited feedback with accurate distribution learning
| Autor: | Eren Manis, Iman Marivani, Ali Emirhan Kurt, Dariush Kari |
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| Rok vydání: | 2017 |
| Předmět: |
Signal processing
Likelihood assignment Data stream mining Computer science Real-time computing Process (computing) Limited feedback Exponential family 020206 networking & telecommunications Anomaly detection 02 engineering and technology Sense (electronics) E-learning Online learning 0202 electrical engineering electronic engineering information engineering Range (statistics) 020201 artificial intelligence & image processing Anomaly (physics) Algorithm Signal detection |
| Zdroj: | SIU Proceedings of the IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017 |
| DOI: | 10.1109/siu.2017.7960595 |
| Popis: | Date of Conference: 15-18 May 2017 Conference Name: IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017 We propose a high-performance algorithm for sequential anomaly detection. The proposed algorithm sequentially runs over data streams, accurately estimates the nominal distribution using exponential family and then declares an anomaly when the assigned likelihood of the current observation is less than a threshold. We use the estimated nominal distribution to assign a likelihood to the current observation and employ limited feedback from the end user to adjust the threshold. The high performance of our algorithm is due to accurate estimation of the nominal distribution, where we achieve this by preventing anomalous data to corrupt the update process. Our method is generic in the sense that it can operate successfully over a wide range of data distributions. We demonstrate the performance of our algorithm with respect to the state-of-the-art over time varying distributions. |
| Databáze: | OpenAIRE |
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