Toward Personalized Deceptive Signaling for Cyber Defense Using Cognitive Models
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 01, 2020
- Source ID
- 10.1111/tops.12513
Entities
People
- Christian Lebiere
- Cleotilde Gonzalez
- Edward A Cranford
- Milind Tambe
- Palvi Aggarwal
- Sarah Cooney
Organizations
- Army Research Office
- Carnegie Mellon University
- Harvard University
- University of Southern California