Human-Computer Decision Systems Poster (SEI 2015 Research Review)
• Poster
Describes work to use learning theory advances to account for persistent human expert teams and experiments to improve the human-computer decision systems.
Publisher
Software Engineering Institute
Abstract
Security decision systems aim to distinguish malicious activity from benign and often use a combination of human experts and automated analysis, including machine learning (ML). Systems using only human experts scale poorly; pure ML systems are susceptible to structured attacks by adversaries and, in most cases, have unsatisfactory performance on their own.
Part of a Collection
SEI 2015 Research Review Artifacts