MLIDS, a Machine Learning Intrusion Detection System

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-06-M-6661
Agency Tracking Number: F061-018-0146
Amount: $99,961.00
Phase: Phase I
Program: SBIR
Awards Year: 2006
Solicitation Year: 2006
Solicitation Topic Code: AF06-018
Solicitation Number: 2006.1
Small Business Information
33 Thornwood Drive, Suite 500, Ithaca, NY, 14850
DUNS: 101321479
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Daniel Tingstrom
 Computer Scientist
 (607) 257-1975
Business Contact
 Richard Smith
Title: Controller
Phone: (607) 257-1975
Research Institution
High-fidelity simulation environments using Distributed Mission Operations (DMO) may be attacked by enemies wishing to subvert the simulation performance and results. To detect, mitigate, and inoculate against such attacks, ATC-NY, in collaboration with Architecture Technology Corporation and Cornell University Professor Thorsten Joachims, will develop the Machine Learning Intrusion Detection System (MLIDS). We will locate specific features in High Level Architecture (HLA) and Distributed Interactive Simulation (DIS) that prove to be significant when attacks occur, and build HLA and DIS profiles that separate these features’ values into two categories: when attacks take place and when they do not take place. MLIDS will use Support Vector Machines (SVMs), a new learning system based on recent advances in statistical learning theory, to build profiles for HLA and DIS and detect malicious DMO network traffic in real-time. MLIDS will alert the network administrator to abnormal—and hence possibly malicious—traffic in real-time and provide guidance in dealing with attacks. To create MLIDS, the ATC-NY team will develop novel technologies for classifying network intrusions in HLA and DIS simulation environments.

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

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