  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 is practical
it is important to be able to reason about the boundaries of its applicability.
Hence, we need to be able to define network defense tasks that capture sets of
adversarial tactics, techniques, and procedures (TTPs); quality of service
(QoS) requirements; and TTPs available to defenders. Furthermore, the
abstractions to define these tasks must be extensible; must be backed by
well-defined semantics that allow us to reason about distributions of
environments; and should enable the generation of data and experiences from
which an agent can learn.
  Our approach named Network Environment Design for Autonomous Cyberdefense
inspired the architecture of FARLAND, a Framework for Advanced Reinforcement
Learning for Autonomous Network Defense, which we use at MITRE to develop RL
network defenders that perform blue actions from the MITRE Shield matrix
against attackers with TTPs that drift from MITRE ATT&CK TTPs.
