Tartakovsky a g nikiforov i v and basseville m 2014

Info icon This preview shows pages 70–72. Sign up to view the full content.

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
Image of page 70

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

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.
Image of page 71
Image of page 72
This is the end of the preview. Sign up to access the rest of the document.
  • Spring '12
  • Kushal Kanwar
  • Graph Theory, Statistical hypothesis testing, Imperial College Press, applicable copyright law

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern