Oppositional human factors in cybersecurity: A preliminary analysis of affective states

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Authors
Ferguson-Walter, Kimberly J.
Gutzwiller, Robert S.
Scott, Dakota
Johnson, Craig J.
Advisors
Issue Date
2021-11-15
Type
Conference paper
Keywords
Psychology , Human factors , Critical infrastructure , Task analysis , Computer security , Software engineering , Deception , Oppositional human factors , Qualitative data analysis , Affect and emotions
Research Projects
Organizational Units
Journal Issue
Citation
K. J. Ferguson-Walter, R. S. Gutzwiller, D. D. Scott and C. J. Johnson, "Oppositional Human Factors in Cybersecurity: A Preliminary Analysis of Affective States," 2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW), 2021, pp. 153-158, doi: 10.1109/ASEW52652.2021.00040.
Abstract

The need for cyber defense research is growing as more cyber-attacks are directed at critical infrastructure and other sensitive networks. Traditionally, the focus has been on hardening system defenses. However, other techniques are being explored including cyber and psychological deception which aim to negatively impact the cognitive and emotional state of cyber attackers directly through the manipulation of network characteristics. In this study, we present a preliminary analysis of survey data collected following a controlled experiment in which over 130 professional red teamers participated in a network penetration task that included cyber deception and psychological deception manipulations [7]. Thematic and inductive analysis of previously un-analyzed open-ended survey responses revealed factors associated with affective states. These preliminary results are a first step in our analysis efforts and show that there are potentially several distinct dimensions of cyber-behavior that induce negative affective states in cyber attackers, which may serve as potential avenues for supplementing traditional cyber defense strategies.

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Publisher
IEEE
Journal
Book Title
Series
2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW);2021
PubMed ID
DOI
ISSN
2151-0830
EISSN