Tartakovsky a g nikiforov i v and basseville m 2014

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Tartakovsky, A. G., Nikiforov, I. V. and Basseville, M. (2014). Sequential Analysis: Hypothesis Testing and Change-Point Detection , Statistics (Chapman & Hall/CRC, Boca Raton, FL). Tartakovsky, A. G., Pollak, M. and Polunchenko, A. S. (2012). Third-order asymptotic optimality of the generalized Shiryaev–Roberts changepoint detection procedures, Theor. Probab. Appl. 56 , 3, pp. 457–484. Tartakovsky, A. G. and Polunchenko, A. S. (2007). Decentralized quickest change detection in distributed sensor systems with applications to information assurance and counter terrorism, in Proceedings of the 13th Annual Army Conference on Applied Statistics (Rice University, Houston, TX). Tartakovsky, A. G. and Polunchenko, A. S. (2008). Quickest changepoint detec- tion in distributed multisensor systems under unknown parameters, in Pro- ceedings of the 11th IEEE International Conference on Information Fusion (Cologne, Germany). Tartakovsky, A. G., Polunchenko, A. S. and Sokolov, G. (2013). Efficient computer network anomaly detection by changepoint detection methods, IEEE J. Sel. Top. Signal Process. 7 , 1, pp. 4–11. Tartakovsky, A. G., Rozovskii, B. L., Bla´ zek, R. B. and Kim, H. (2006a). Detec- tion of intrusions in information systems by sequential change-point meth- ods, Stat. Method. 3 , 3, pp. 252–293. Tartakovsky, A. G., Rozovskii, B. L., Bla´ zek, R. B. and Kim, H. (2006b). A novel approach to detection of intrusions in computer networks via adap- tive sequential and batch-sequential change-point detection methods, IEEE Tran. Signal Proc. 54 , 9, pp. 3372–3382. Tartakovsky, A. G. and Veeravalli, V. V. (2004). Change-point detection in mul- tichannel and distributed systems, in N. Mukhopadhyay, S. Datta and S. Chattopadhyay (eds), Applied Sequential Methodologies: Real-World Exam- ples with Data Analysis, Statistics: a Series of Textbooks and Monographs , Vol. 173 (Marcel Dekker, Inc, New York), pp. 339–370. Tartakovsky, A. G. and Veeravalli, V. V. (2005). General asymptotic Bayesian theory of quickest change detection, Theor. Probab. Appl. 49 , 3, pp. 458– 497. Copyright © 2014. Imperial College Press. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. EBSCO Publishing : eBook Collection (EBSCOhost) - printed on 2/16/2016 3:37 AM via CGC-GROUP OF COLLEGES (GHARUAN) AN: 779681 ; Heard, Nicholas, Adams, Niall M..; Data Analysis for Network Cyber-security Account: ns224671
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Chapter 3 Statistical Detection of Intruders Within Computer Networks Using Scan Statistics Joshua Neil, Curtis Storlie, Curtis Hash and Alex Brugh Los Alamos National Laboratory PO BOX 1663, Los Alamos, New Mexico, 87545, USA [email protected] We introduce a computationally scalable method for detecting small anoma- lous subgraphs in large, time-dependent graphs. This work is motivated by, and validated against, the challenge of identifying intruders operating inside enterprise-sized computer networks with 500 million communication events per day. Every observed edge (time series of communications between each pair of computers on the network) is modeled using observed and hidden Markov models to establish baselines of behavior for purposes of anomaly detection.
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