DDDAS/ITR: A data mining and exploration middleware for grid and distributed computing

Jon B. Weissman; Vipin Kumar; Varun Chandola; Eric Eilertson; Levent Ertoz; Gyorgy Simon; Seonho Kim; Jinoh Kim

doi:10.1007/978-3-540-72584-8_159

DDDAS/ITR: A data mining and exploration middleware for grid and distributed computing

Jon B. Weissman
, Vipin Kumar
, Varun Chandola
, Eric Eilertson
, Levent Ertoz
, Gyorgy Simon
, Seonho Kim
, Jinoh Kim

Department of Computer Science and Engineering

University of Minnesota Twin Cities

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

We describe our project that marries data mining together with Grid computing. Specifically, we focus on one data mining application - the Minnesota Intrusion Detection System (MINDS), which uses a suite of data mining based algorithms to address different aspects of cyber security including malicious activities such as denial-of-service (DoS) traffic, worms, policy violations and inside abuse. MINDS has shown great operational success in detecting network intrusions in several real deployments. In sophisticated distributed cyber attacks using a multitude of wide-area nodes, combining the results of several MINDS instances can enable additional early-alert cyber security. We also describe a Grid service system that can deploy and manage multiple MINDS instances across a wide-area network.

Original language	English
Title of host publication	Computational Science - ICCS 2007 - 7th International Conference, Proceedings, Part I
Publisher	Springer Verlag
Pages	1222-1229
Number of pages	8
ISBN (Print)	9783540725831
DOIs	https://doi.org/10.1007/978-3-540-72584-8_159
State	Published - 2007
Event	7th International Conference on Computational Science, ICCS 2007 - Beijing, China Duration: May 27 2007 → May 30 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4487 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Computational Science, ICCS 2007
Country/Territory	China
City	Beijing
Period	05/27/07 → 05/30/07

Keywords

Data mining
Grid computing

Access to Document

10.1007/978-3-540-72584-8_159

Cite this

Weissman, J. B., Kumar, V., Chandola, V., Eilertson, E., Ertoz, L., Simon, G., Kim, S., & Kim, J. (2007). DDDAS/ITR: A data mining and exploration middleware for grid and distributed computing. In Computational Science - ICCS 2007 - 7th International Conference, Proceedings, Part I (pp. 1222-1229). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4487 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-540-72584-8_159

Weissman, Jon B. ; Kumar, Vipin ; Chandola, Varun et al. / DDDAS/ITR : A data mining and exploration middleware for grid and distributed computing. Computational Science - ICCS 2007 - 7th International Conference, Proceedings, Part I. Springer Verlag, 2007. pp. 1222-1229 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{1fd127e43b564fec9a61d65fcb963e80,

title = "DDDAS/ITR: A data mining and exploration middleware for grid and distributed computing",

abstract = "We describe our project that marries data mining together with Grid computing. Specifically, we focus on one data mining application - the Minnesota Intrusion Detection System (MINDS), which uses a suite of data mining based algorithms to address different aspects of cyber security including malicious activities such as denial-of-service (DoS) traffic, worms, policy violations and inside abuse. MINDS has shown great operational success in detecting network intrusions in several real deployments. In sophisticated distributed cyber attacks using a multitude of wide-area nodes, combining the results of several MINDS instances can enable additional early-alert cyber security. We also describe a Grid service system that can deploy and manage multiple MINDS instances across a wide-area network.",

keywords = "Data mining, Grid computing",

author = "Weissman, \{Jon B.\} and Vipin Kumar and Varun Chandola and Eric Eilertson and Levent Ertoz and Gyorgy Simon and Seonho Kim and Jinoh Kim",

year = "2007",

doi = "10.1007/978-3-540-72584-8\_159",

language = "English",

isbn = "9783540725831",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "1222--1229",

booktitle = "Computational Science - ICCS 2007 - 7th International Conference, Proceedings, Part I",

address = "Germany",

note = "7th International Conference on Computational Science, ICCS 2007 ; Conference date: 27-05-2007 Through 30-05-2007",

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Weissman, JB, Kumar, V, Chandola, V, Eilertson, E, Ertoz, L, Simon, G, Kim, S & Kim, J 2007, DDDAS/ITR: A data mining and exploration middleware for grid and distributed computing. in Computational Science - ICCS 2007 - 7th International Conference, Proceedings, Part I. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4487 LNCS, Springer Verlag, pp. 1222-1229, 7th International Conference on Computational Science, ICCS 2007, Beijing, China, 05/27/07. https://doi.org/10.1007/978-3-540-72584-8_159

DDDAS/ITR: A data mining and exploration middleware for grid and distributed computing. / Weissman, Jon B.; Kumar, Vipin; Chandola, Varun et al.
Computational Science - ICCS 2007 - 7th International Conference, Proceedings, Part I. Springer Verlag, 2007. p. 1222-1229 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4487 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - DDDAS/ITR

T2 - 7th International Conference on Computational Science, ICCS 2007

AU - Weissman, Jon B.

AU - Kumar, Vipin

AU - Chandola, Varun

AU - Eilertson, Eric

AU - Ertoz, Levent

AU - Simon, Gyorgy

AU - Kim, Seonho

AU - Kim, Jinoh

PY - 2007

Y1 - 2007

N2 - We describe our project that marries data mining together with Grid computing. Specifically, we focus on one data mining application - the Minnesota Intrusion Detection System (MINDS), which uses a suite of data mining based algorithms to address different aspects of cyber security including malicious activities such as denial-of-service (DoS) traffic, worms, policy violations and inside abuse. MINDS has shown great operational success in detecting network intrusions in several real deployments. In sophisticated distributed cyber attacks using a multitude of wide-area nodes, combining the results of several MINDS instances can enable additional early-alert cyber security. We also describe a Grid service system that can deploy and manage multiple MINDS instances across a wide-area network.

AB - We describe our project that marries data mining together with Grid computing. Specifically, we focus on one data mining application - the Minnesota Intrusion Detection System (MINDS), which uses a suite of data mining based algorithms to address different aspects of cyber security including malicious activities such as denial-of-service (DoS) traffic, worms, policy violations and inside abuse. MINDS has shown great operational success in detecting network intrusions in several real deployments. In sophisticated distributed cyber attacks using a multitude of wide-area nodes, combining the results of several MINDS instances can enable additional early-alert cyber security. We also describe a Grid service system that can deploy and manage multiple MINDS instances across a wide-area network.

KW - Data mining

KW - Grid computing

UR - https://www.scopus.com/pages/publications/37249046101

U2 - 10.1007/978-3-540-72584-8_159

DO - 10.1007/978-3-540-72584-8_159

M3 - Conference contribution

AN - SCOPUS:37249046101

SN - 9783540725831

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 1222

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BT - Computational Science - ICCS 2007 - 7th International Conference, Proceedings, Part I

PB - Springer Verlag

Y2 - 27 May 2007 through 30 May 2007

ER -

Weissman JB, Kumar V, Chandola V, Eilertson E, Ertoz L, Simon G et al. DDDAS/ITR: A data mining and exploration middleware for grid and distributed computing. In Computational Science - ICCS 2007 - 7th International Conference, Proceedings, Part I. Springer Verlag. 2007. p. 1222-1229. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-72584-8_159

DDDAS/ITR: A data mining and exploration middleware for grid and distributed computing

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