Three multi-modal datasets capture 59 skilled attackers' full operational traces (keystrokes, shell history, PCAP, surveys) in a simulated enterprise network, with labels designed to reveal cognitive biases.
Game of Travesty: Decoy-based Psychological Cyber Deception for Proactive Human Agents
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abstract
The concept of cyber deception has been receiving emerging attention. The development of cyber defensive deception techniques requires interdisciplinary work, among which cognitive science plays an important role. In this work, we adopt a signaling game framework between a defender and a human agent to develop a cyber defensive deception protocol that takes advantage of the cognitive biases of human decision-making using quantum decision theory to combat insider attacks (IA). The defender deceives an inside human attacker by luring him to access decoy sensors via generators producing perceptions of classical signals to manipulate the human attacker's psychological state of mind. Our results reveal that even without changing the classical traffic data, strategically designed generators can result in a worse performance for defending against insider attackers in identifying decoys than the ones in the deceptive scheme without generators, which generate random information based on input signals. The proposed framework leads to fundamental theories in designing more effective signaling schemes.
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Guarding Against Malicious Biased Threats (GAMBiT) Experiments: Revealing Cognitive Bias in Human-Subjects Red-Team Cyber Range Operations
Three multi-modal datasets capture 59 skilled attackers' full operational traces (keystrokes, shell history, PCAP, surveys) in a simulated enterprise network, with labels designed to reveal cognitive biases.