DOODLEBUG: A Distributed, Peer-to-Peer Approach to Cross-Domain Attack Correlation

Award Information
Agency: Department of Homeland Security
Branch: N/A
Contract: NBCHC050002
Agency Tracking Number: 0421009
Amount: $99,743.00
Phase: Phase I
Program: SBIR
Awards Year: 2004
Solicitation Year: N/A
Solicitation Topic Code: N/A
Solicitation Number: N/A
Small Business Information
211 N 1st St., Suite 300, Minneapolis, MN, 55401
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Robert Goldman
 Senior Principal Research Scientist
 (612) 384-3454
Business Contact
 Harry Funk
Title: Vice President, Research and Development
Phone: (612) 578-7438
Research Institution
SIFT`s DOODLEBUG approach provides a revolutionary approach to intrusion event correlation and fusion. Existing correlation systems are brittle, mostly rule-based systems that provide centralized correlation of intrusion events from very restricted areas. They require access to confidential information of the systems whose events they correlate, share those systems` false positive problems, and are difficult to configure and operate. By contrast, DOODLEBUG provides a lightweight event correlation solution that will operate over extremely large areas (e.g., continent-wide). The DOODLEBUG approach will build a robust, redundant, decentralized network for event correlation, producing rich results by incorporate very large amounts of information. Using large amounts of information, and combining it with relatively simple, and computationally inexpensive operations, DOODLEBUG will enable detection and identification of both known and novel attacks. In order to incorporate these large numbers of nodes, DOODLEBUG must stretch across not just administrative domains, but enterprises as well. To make this possible, DOODLEBUG will incorporate techniques for robust computation even in the face of malicious network members, and will provide strong assurances of the privacy of network nodes. DOODLEBUG will do this by combining a novel re-framing of the correlation problem with existing techniques for robust distributed computation and privacy protection.

* Information listed above is at the time of submission. *

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