Exploiting Base Rate Neglect to Disrupt and Distract Cyber Attackers

Oppositional human factors (OHF) seeks to exploit tendencies in human thinking to disrupt cyber attackers. One tendency is base rate neglect (BRN), where individuals overlook the likelihood of an event during reasoning, and instead base judgements on salient surface details. An expert sample of cyber red teamers completed cognitive bias survey measures, followed by missions in a cyber range. In the range, features on a server consistent with a vulnerability but out of context (extremely low base rate) were used to test whether these experts would ignore such base rates. BRN was found, including meaningful, significant performance reductions, suggesting a real, valid path for OHF techniques. Further, this approach can be employed even where bias susceptibility predictions for an attacker are unavailable.

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Publication Details

Journal
ScholarSpace (University of Hawaii at Manoa)
Published
2025-12-23
Primary Topic
Human-Automation Interaction and Safety
Type
article
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article

Exploiting Base Rate Neglect to Disrupt and Distract Cyber Attackers

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Exploiting Base Rate Neglect to Disrupt and Distract Cyber Attackers

Joel Hypolite, Robert S. Gutzwiller, Max Slocum, Sean Guarino, Curt Wu, Susan Latiff, Michael E. Locasto, M. Sieffert, Benjamin Clegg, Matthew Revelle, K Raghav Bhat, Scott Harrison, Spencer Lynn, David Kelle
article en

Abstract

Oppositional human factors (OHF) seeks to exploit tendencies in human thinking to disrupt cyber attackers. One tendency is base rate neglect (BRN), where individuals overlook the likelihood of an event during reasoning, and instead base judgements on salient surface details. An expert sample of cyber red teamers completed cognitive bias survey measures, followed by missions in a cyber range. In the range, features on a server consistent with a vulnerability but out of context (extremely low base rate) were used to test whether these experts would ignore such base rates. BRN was found, including meaningful, significant performance reductions, suggesting a real, valid path for OHF techniques. Further, this approach can be employed even where bias susceptibility predictions for an attacker are unavailable.

ScholarSpace (University of Hawaii at Manoa)
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Openalex Percentile: Top 28%
Human-Automation Interaction and Safety
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