Tractable Defense against Advanced Persistent Threats in Networked Settings
Mean-field heuristic makes Boolean Dynamical Systems defense against APTs tractable, exactly computing the value function under max-entropy assumptions.
The paper models APT network defense via Boolean Dynamical Systems, capturing attack stealth, noisy IDS observations, lateral movement, and defender hardening trade-offs. Because the emergent value function is computationally intractable with respect to network size, the authors propose a mean-field-inspired heuristic value function. They prove the heuristic is an exact computation under a maximum-entropy state-estimate assumption and numerically evaluate its quality as entropy assumptions are violated.
- Models APT defense with stealth, lateral movement, noisy IDS, and hardening costs
- Optimal value function intractable with network size; heuristic restores tractability
- Heuristic exact when state-estimate distribution maximizes entropy
Full article168 words · extracted from arxiv.org · click to collapse
Recently, the theory of Boolean Dynamical Systems was proposed to study the decision theory surrounding the defense of computer networks against Advanced Persistent Threats (APTs). Boolean Dynamical Systems naturally capture four first principle primitives of APTs: the stealthy nature of attacks, limited and noisy information from automated systems like intrusion detection systems, lateral movement after the attacker penetrates into the network, and the defender's ability to secure a subset of computers at any time at the loss of resources such as system uptime. Currently, doing optimal/heuristic control in a computationally tractable manner is not possible because the emergent value function is computationally intractable (with respect to the network size). To resolve this, we propose a mean-field analysis inspired heuristic value function. We prove that our proposed heuristic is based on an exact computation of the value function under the assumption that the underlying state estimate distribution maximizes entropy. We numerically evaluate the quality of our heuristic as parameterized by the degree to which the entropy assumptions are violated.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.15614