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 at Buffalo

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

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{cc63514f02bd40d089be8c8a7ce4eae5,

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

SN - 9783540725831

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

SP - 1222

EP - 1229

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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