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In-network PCA and anomaly detection

Huang Ling, Nguyen XuanLong, Garofalakis Minos, Jordan Michael I., Joseph Anthony, Taft Nina

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URIhttp://purl.tuc.gr/dl/dias/ABF8CEBF-57C5-4563-B3FC-989DAAE7238B-
Identifierhttp://papers.nips.cc/paper/3156-in-network-pca-and-anomaly-detection.pdf-
Languageen-
Extent8 pagesen
TitleIn-network PCA and anomaly detectionen
CreatorHuang Lingen
CreatorNguyen XuanLongen
CreatorGarofalakis Minosen
CreatorΓαροφαλακης Μινωςel
CreatorJordan Michael I.en
CreatorJoseph Anthonyen
CreatorTaft Ninaen
Content SummaryWe 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 ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-12-01-
Date of Publication2006-
SubjectDatabases en
SubjectManagementen
Bibliographic CitationL. 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

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