Institutional Repository
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI | http://purl.tuc.gr/dl/dias/ABF8CEBF-57C5-4563-B3FC-989DAAE7238B | - |
| Identifier | http://papers.nips.cc/paper/3156-in-network-pca-and-anomaly-detection.pdf | - |
| Language | en | - |
| Extent | 8 pages | en |
| Title | In-network PCA and anomaly detection | en |
| Creator | Huang Ling | en |
| Creator | Nguyen XuanLong | en |
| Creator | Garofalakis Minos | en |
| Creator | Γαροφαλακης Μινως | el |
| Creator | Jordan Michael I. | en |
| Creator | Joseph Anthony | en |
| Creator | Taft Nina | en |
| Content Summary | We consider the problem of network anomaly detection in large distributed systems. In this setting, Principal Component Analysis (PCA) has been proposed as a method for discovering anomalies by continuously tracking the projection of the data onto a residual subspace. This method was shown to work well empirically in highly aggregated networks, that is, those with a limited number of large nodes and at coarse time scales. This approach, however, has scalability limitations. To overcome these limitations, we develop a PCA-based anomaly detector in which adaptive local data filters send to a coordinator just enough data to enable accurate global detection. Our method is based on a stochastic matrix perturbation analysis that characterizes the tradeoff between the accuracy of anomaly detection and the amount of data communicated over the network. | en |
| Type of Item | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Full Paper | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2015-12-01 | - |
| Date of Publication | 2006 | - |
| Subject | Databases | en |
| Subject | Management | en |
| Bibliographic Citation | L. Huang, X. Nguyen, M. Garofalakis, M. I. Jordan, A. Joseph and N. Taft, "In-network PCA and anomaly detection", in 20th Annual Conference on Neural Information Processing Systems, 2006. | en |
Copyright © DIAS 2013
