Date of Award

3-22-2019

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering

Department

Department of Electrical and Computer Engineering

First Advisor

Brett Borghetti, PhD

Abstract

Cyber defense analysts face the challenge of validating machine generated alerts regarding network-based security threats. Operations tempo and systematic manpower issues have increased the importance of these individual analyst decisions, since they typically are not reviewed or changed. Analysts may not always be confident in their decisions. If confidence can be accurately assessed, then analyst decisions made under low confidence can be independently reviewed and analysts can be offered decision assistance or additional training. This work investigates the utility of using neurophysiological and behavioral correlates of decision confidence to train machine learning models to infer confidence in analyst decisions. Electroencephalography (EEG) and behavioral data was collected from eight participants in a two-task human-subject experiment and used to fit several popular classifiers. Results suggest that for simple decisions, it is possible to classify analyst decision confidence using EEG signals. However, more work is required to evaluate the utility of EEG signals for classification of decision confidence in complex decisions.

AFIT Designator

AFIT-ENG-MS-19-M-028

DTIC Accession Number

AD1075066

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