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

被引：0

作者：

Weissman, Jon B. ^{[1
]}

Kumar, Vipin ^{[1
]}

Chandola, Varun ^{[1
]}

Eilertson, Eric ^{[1
]}

Ertoz, Levent ^{[1
]}

Simon, Gyorgy ^{[1
]}

Kim, Seonho ^{[1
]}

Kim, Jinoh ^{[1
]}

机构：

[1] Univ Minnesota Twin Cities, Dept Comp Sci & Engn, Minneapolis, MN 55455 USA

来源：

COMPUTATIONAL SCIENCE - ICCS 2007, PT 1, PROCEEDINGS | 2007年 / 4487卷

关键词：

data mining; Grid computing;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：1222 / +

页数：2