Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
Adversary emulation is commonly used to test cyber defense performance against known threats to organizations. However, designing attack strategies is an expensive and unreliable manual process, based on subjective evaluation of the state of a network. In this paper, we propose the design of adversarial human-like cognitive models that are dynamic, adaptable, and have the ability to learn from experience. A cognitive model is built according to the theoretical principles of Instance-Based Learning Theory (IBLT) of experiential choice in dynamic tasks. In a simulation experiment, we compared the predictions of an IBL attacker with a carefully designed efficient but deterministic attacker attempting to access an operational server in a network. The results suggest that an IBL cognitive model that emulates human behavior can be a more challenging adversary for defenders than the carefully crafted optimal attack strategies. These insights can be used to inform future adversary emulation efforts and cyber defender training.
Recommended Citation
Du, Yinuo; Prebot, Baptiste; Xi, Xiaoli; and Gonzalez, Cleotilde, "A Cyber-War Between Bots: Human-Like Attackers are More Challenging for Defenders than Deterministic Attackers" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 3.
https://aisel.aisnet.org/hicss-56/da/cyber_deception/3
A Cyber-War Between Bots: Human-Like Attackers are More Challenging for Defenders than Deterministic Attackers
Online
Adversary emulation is commonly used to test cyber defense performance against known threats to organizations. However, designing attack strategies is an expensive and unreliable manual process, based on subjective evaluation of the state of a network. In this paper, we propose the design of adversarial human-like cognitive models that are dynamic, adaptable, and have the ability to learn from experience. A cognitive model is built according to the theoretical principles of Instance-Based Learning Theory (IBLT) of experiential choice in dynamic tasks. In a simulation experiment, we compared the predictions of an IBL attacker with a carefully designed efficient but deterministic attacker attempting to access an operational server in a network. The results suggest that an IBL cognitive model that emulates human behavior can be a more challenging adversary for defenders than the carefully crafted optimal attack strategies. These insights can be used to inform future adversary emulation efforts and cyber defender training.
https://aisel.aisnet.org/hicss-56/da/cyber_deception/3