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Computer network security / Data analysis / Anomaly detection / Intrusion detection system / Local outlier factor / Network performance / Misuse detection / Robust random early detection / Outlier / Statistics / Data security / Data mining


Document Date: 2002-10-29 14:04:58

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City

Edmonton / Mahalanobis / Los Alamitos / Lincoln / San Jose / Identifying Density / Toulouse / Chicago / /

Company

Added Class Precision / High Precision High / SMOTEBoost / CSC / IEEE Computer Society Press / John Wiley and Sons / SMOTEBoost Precision Boosting / Computer Science Laboratory / Oracle / Riptech Inc. / SRI International / Evaluating Intrusion Detection Systems / Microsoft / /

Country

France / Canada / United States / /

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Event

Product Issues / Product Recall / Reorganization / /

Facility

Minnesota Supercomputing Institute / EE/CSC Building University of Minnesota / Mississippi State University / Laboratory IDS Evaluation / L. Hall / University of Minnesota / /

IndustryTerm

temporal data mining algorithms / information processing / on-line analysis / frequent itemset generation algorithm / presented anomaly detection algorithms / packet filtering tool / insurance fraud detection / intrusion detection algorithm / larger Web / data mining algorithms / information systems / Internet accessibility falls / tcptrace software utility / misuse detection algorithms / classification algorithm / machine learning algorithm / all developed algorithms / windows networking / state-of-theart intrusion detection systems / computing / data mining community / visualization tool / data mining techniques / anomaly detection tool / anomaly detection algorithms / association-based classification algorithm / on-line and distributed intrusion detection / software tool / outlier detection algorithms / signature-based intrusion detection systems / unsupervised learning algorithm / anomaly/outlier detection algorithms / data mining / serial data mining algorithms / intrusion detection algorithms / rare class learning algorithms / classification algorithms / mining / tcptrace utility software / recent outlier detection algorithms / learning algorithm / unsupervised support vector machine algorithms / /

OperatingSystem

DoS / /

Organization

Pang-Nig Tan Computer Science Department / University of Minnesota / Minneapolis / Mississippi State University / Army High Performance Computing Research Center / Computer Emergency Response Team/Coordination Center / Department of Computer Science / Minnesota Supercomputing Institute / Asiatic Society of Benagal / /

Person

Aleksandar Lazarevic / R. P. Lippmann / R. K. Cunningham / Paul Dokas / Daniel Barbara / Richard Lippmann / R. Agarwal / V / M. Joshi / V / Levent Ertoz / /

Position

D. J. / analyst / /

Product

KDDCUP’99 data Added Class Precision / High F-value SMOTEBoost Recall SMOTEBoost Precision Boosting Recall Boosting Precision / /

ProvinceOrState

Minnesota / California / Illinois / /

PublishedMedium

the Recent Advances / Machine Learning / IEEE Transactions on Software Engineering / /

Technology

applying data mining algorithms / unsupervised learning algorithm / recent outlier detection algorithms / classification algorithm / supervised SVM algorithm / anomaly/outlier detection algorithms / misuse detection algorithms / two-stage learning algorithm / SVM algorithm / unsupervised algorithm / intrusion detection algorithm / Fuzzy Logic / association-based classification algorithm / Anomaly Detection Algorithms / serial data mining algorithms / machine learning algorithm / VPN / Machine Learning / UDP / intrusion detection algorithms / temporal data mining algorithms / learning algorithm / vector machine algorithms / data mining algorithms / presented anomaly detection algorithms / classification algorithms / Data Mining / rare class learning algorithms / outlier detection algorithms / frequent itemset generation algorithm / /

URL

www.tcptrace.org / www.snort.org / /

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