DDDAS/ITR: A Data Mining and Exploration Middleware for Grid and Distributed Computing

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.

• Type: Conference
• Year: 2007
• Where: ICCS
• Authors: Jon B. Weissman, Vipin Kumar, Varun Chandola, Eric Eilertson, Levent Ertöz, György J. Simon, Seonho Kim, Jinoh Kim
• Keywords: Cyber Security, Data Mining, ICCS 2007, MINDS Instances