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Toward Personalized Deceptive Signaling for Cyber Defense Using Cognitive Models
Author(s) -
Cranford Edward A.,
Gonzalez Cleotilde,
Aggarwal Palvi,
Cooney Sarah,
Tambe Milind,
Lebiere Christian
Publication year - 2020
Publication title -
topics in cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 56
eISSN - 1756-8765
pISSN - 1756-8757
DOI - 10.1111/tops.12513
Subject(s) - computer science , signaling game , trace (psycholinguistics) , rationality , insider , cognition , deception , computer security , game theory , human–computer interaction , cognitive science , artificial intelligence , psychology , social psychology , neuroscience , linguistics , philosophy , political science , law , economics , microeconomics
Recent research in cybersecurity has begun to develop active defense strategies using game‐theoretic optimization of the allocation of limited defenses combined with deceptive signaling. These algorithms assume rational human behavior. However, human behavior in an online game designed to simulate an insider attack scenario shows that humans, playing the role of attackers, attack far more often than predicted under perfect rationality. We describe an instance‐based learning cognitive model, built in ACT‐R, that accurately predicts human performance and biases in the game. To improve defenses, we propose an adaptive method of signaling that uses the cognitive model to trace an individual's experience in real time. We discuss the results and implications of this adaptive signaling method for personalized defense.