Computer Network Operations (CNO) for Ground-based Midcourse Defense (GMD)

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: W9113M-05-C-0064
Agency Tracking Number: 044-1391
Amount: $99,958.00
Phase: Phase I
Program: SBIR
Awards Year: 2005
Solicitation Year: 2004
Solicitation Topic Code: MDA04-092
Solicitation Number: 2004.4
Small Business Information
TORCH TECHNOLOGIES, INC.
2227 Drake Avenue, Suite 27, Huntsville, AL, 35805
DUNS: 122515708
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Mike Muratet
 Principal Investigator
 (256) 319-6000
 mike.muratet@torchtechnologies.com
Business Contact
 Kenneth Lones
Title: Contracts Administrator
Phone: (256) 319-6000
Email: kenneth.lones@torchtechnologies.com
Research Institution
N/A
Abstract
Most computer network intrusion detection (ID) systems are based on the detection of a priori patterns determined during security audits, or more often by post-attack forensic analysis. By all estimates, thousands of new attack modes are identified each year which cause damage until they are discovered. The problem is compounded by the threat of stealthy `insider' attacks which may go undiscovered for extended periods. The evolution of the Ballistic Missile Defense System (BMDS) will introduce new external components (e.g., Aegis) into what has been a closed system. These additional elements will expand and improve the capabilities of the BMDS, but they will also introduce significant new Computer Network Operations (CNO) concerns. Improved methods are needed that can provide increased protection. Torch Technologies will examine the feasibility of integrating Maximum Likelihood Adaptive Neural System (MLANS) technology into agent-based intrusion detection (ID) systems. Through adaptive, multidimensional statistical modeling of network traffic within the system, MLANS-capable ID systems will increase the detection rates of internal and external malicious activity, reduce detection time, and decrease false positives. Because it is a ML technique, MLANS achieves the Cramer-Rao bound for the fastest possible learning and accuracy, and the Bayes Error for the least possible error rate. Feasibility analysis will focus on extending the MLANS algorithm to incorporate the Weibull density in order to model the chaotic nature of inter-arrival and service times and other parameters poorly modeled by Gaussian mixtures.

* information listed above is at the time of submission.

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