Understanding the Effects of Cyber Warfare on Human Operators

Abstract

The goal of this YIP research effort was to develop custom machine learning classifiers that take input from fNIRS and other physiological sensors in order to predict the changing mental states of computer operators (e.g., predict cognitive load, levels of trust and suspicion, stress). We tested our models in a variety of use case scenarios that are relevant to the cyber domain, in order to better understand the cognitive and emotional states that are related to operator performance in the presence of cyber threats. Over the course of the effort we collected fNIRS data on 160 human subject participants, created and shared custom machine learning code with AFRL and academic colleagues, and published 15 research articles.

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Document Details

Document Type
Technical Report
Publication Date
Oct 31, 2017
Accession Number
AD1060775

Entities

People

  • Leanne Hirshfield

Organizations

  • Syracuse University

Tags

Communities of Interest

  • Autonomy
  • Cyber

DTIC Thesaurus Topics

  • Air Force
  • Bayesian Networks
  • Cognitive Systems Engineering
  • Cognitive Workload
  • Computational Science
  • Cyber Warfare
  • Cyberattacks
  • Data Mining
  • Dimensionality Reduction
  • Human-Computer Interaction
  • Information Operations
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • Psychology
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Irregular Warfare and Special Operations Cyberspace Operations against Adversarial Threats.
  • Medical Imaging.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Cyber