Network Defense is Not a Game
Andres Molina-Markham, Ransom K. Winder, Ahmad Ridley

TL;DR
This paper argues that network defense should be viewed as a collection of adaptable, uncertain games and proposes a framework to design and evaluate AI-based autonomous cyberdefense systems using diverse network environments.
Contribution
It introduces a novel approach to define network defense as distributions of environments, enabling the application of modern AI techniques and the creation of standardized challenges for autonomous cyberdefense.
Findings
Proposes a framework for designing adaptable network defense environments.
Demonstrates the use of reinforcement learning for autonomous network defense.
Develops the FARLAND architecture for practical AI-driven cyberdefense.
Abstract
Research seeks to apply Artificial Intelligence (AI) to scale and extend the capabilities of human operators to defend networks. A fundamental problem that hinders the generalization of successful AI approaches -- i.e., beating humans at playing games -- is that network defense cannot be defined as a single game with a fixed set of rules. Our position is that network defense is better characterized as a collection of games with uncertain and possibly drifting rules. Hence, we propose to define network defense tasks as distributions of network environments, to: (i) enable research to apply modern AI techniques, such as unsupervised curriculum learning and reinforcement learning for network defense; and, (ii) facilitate the design of well-defined challenges that can be used to compare approaches for autonomous cyberdefense. To demonstrate that an approach for autonomous network defense…
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Taxonomy
TopicsAdvanced Malware Detection Techniques · Network Security and Intrusion Detection · Information and Cyber Security
Methodstravel james
