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Proceedings Paper

Modeling approaches for intrusion detection and prevention system return on investment
Author(s): Nandi O. Leslie; Lisa M. Marvel; Joshua Edwards; Kyra Comroe; Gregory Shearer; Lawrence Knachel
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Paper Abstract

Making decisions about intrusion detection and/or prevention system (IDPS) enhancements are often limited to tool effectiveness (i.e., predictive performance). However, in many cases, the tools in an IDPS are operating in information environments, where the malicious behavior is difficult to discern, and computational resources are limited. We develop three novel IDPS performance models motivated by the return on investment (ROI) metric, where each model is designed to compare each tool’s relative contributions to the system-level performance over multiple scenarios and configurations. Each of our approaches combine statistical accuracy metrics and computational resource costs into one model to facilitate decision making on IDPS configurations.

Paper Details

Date Published: 1 May 2017
PDF: 13 pages
Proc. SPIE 10185, Cyber Sensing 2017, 1018502 (1 May 2017); doi: 10.1117/12.2258026
Show Author Affiliations
Nandi O. Leslie, U.S. Army Research Lab. (United States)
Lisa M. Marvel, U.S. Army Research Lab. (United States)
Joshua Edwards, U.S. Army Research Lab. (United States)
Kyra Comroe, U.S. Army Research Lab. (United States)
Gregory Shearer, U.S. Army Research Lab. (United States)
Lawrence Knachel, U.S. Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 10185:
Cyber Sensing 2017
Igor V. Ternovskiy; Peter Chin, Editor(s)

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