Designing Robust Cyber-Defense Agents with Evolving Behavior Trees

Abstract: Modern network defense can benefit from the use of autonomous systems, offloading tedious and time-consuming work to agents with standard and learning-enabled components. These agents, operating on critical network infrastructure, need to be robust and trustworthy to ensure defense against adaptive cyber-attackers and, simultaneously, provide explanations for their actions and network activity. However, learning-enabled components typically use models, such as deep neural networks, that are not transparent in their high-level decision-making leading to assurance challenges. Additionally, cyber-defense agents must execute complex long-term defense tasks in a reactive manner that involve coordination of multiple interdependent subtasks. Behavior trees are known to be successful in modelling interpretable, reactive, and modular agent policies with learning-enabled components. In this paper, we develop an approach to design autonomous cyber defense agents using behavior trees with learning-enabled components, which we refer to as Evolving Behavior Trees (EBTs). We learn the structure of an EBT with a novel abstract cyber environment and optimize learning-enabled components for deployment. The learning-enabled components are optimized for adapting to various cyber-attacks and deploying security mechanisms. The learned EBT structure is evaluated in a simulated cyber environment, where it effectively mitigates threats and enhances network visibility. For deployment, we develop a software architecture for evaluating EBT-based agents in computer network defense scenarios. Our results demonstrate that the EBT-based agent is robust to adaptive cyber-attacks and provides high-level explanations for interpreting its decisions and actions.

• The paper introduces a three-stage framework that integrates behavior tree structure learning, optimization of learning-enabled components, and deployment in simulation environments.
• The methodology achieves a 39% improvement in average reward over state-of-the-art methods, demonstrating enhanced robustness against dynamic cyber-attacks.
• The approach emphasizes interpretability by combining modular, transparent behavior trees with adaptive strategy switching to foster trust in autonomous cyber-defense operations.

Evolving Behavior Trees for Autonomous Cyber-Defense

The paper "Designing Robust Cyber-Defense Agents with Evolving Behavior Trees" by Potteiger et al. presents an innovative approach to designing autonomous cyber-defense agents using Evolving Behavior Trees (EBTs) to improve the robustness and explainability of cyber defenses against dynamic cyber-attacks. This methodology leverages the modular and hierarchical properties of Behavior Trees (BTs) combined with learning-enabled components (LECs) to handle the complexity of long-term network defense effectively.

The authors recognize the challenges of transparency and robustness associated with using neural networks in autonomous agents, especially when these systems must perform complex tasks and adapt to multiple attack strategies. This paper presents a neurosymbolic approach that integrates LECs with the inherent advantages of BTs, which are known for providing modular, reactive, and interpretable policies.

Approach and Methodology

The researchers propose a three-stage framework focused on learning, optimizing, and deploying autonomous cyber-defense agents represented as EBTs:

1. BT Structure Learning: The approach begins by learning the structure of an EBT using an abstract environment named Cyber-Firefighter. This environment abstracts the network defense problem into a pursuit-evasion game, where a defender (the agent) aims to prevent a fire (an attacker) from spreading through a network. Using Genetic Programming (GP), an optimal BT structure is derived, highlighting the efficiency of BTs in managing visibility and control over network threats.
2. Optimization of LECs: To complement the learned BT structure, specific LECs are optimized for adaptability and resilience to attacks. The components include a cyber-agent controller optimized using Reinforcement Learning (RL) and a strategy switching policy trained using supervised learning. The LECs enable the BT to manage an extensive array of actions and adjust to observed attacker behavior dynamically.
3. Integration and Deployment: The final stage integrates these components into a coherent EBT structure deployed within the realistic CybORG environment, specifically using CAGE Challenge Scenario 2. The authors developed a blackboard-based software architecture to facilitate the interaction between EBTs and the CybORG simulation, demonstrating the deployment capability of their approach.

Evaluation and Results

The paper's evaluation metrics demonstrate the robustness of the EBT-based approach against adaptive cyber-attacks. The GP-derived BT structure showed comparable effectiveness to expert-designed BTs in the abstract Cyber-Firefighter environment. When evaluated on CAGE Challenge Scenario 2, the EBT approach with adaptive strategy switching reported a 39% improvement in average reward over a state-of-the-art control method, illustrating enhanced resilience and task performance.

The authors also emphasize the explainability advantage of the EBTs. The BT structure provides a transparent mechanism to monitor critical events, such as strategy transitions or decoy deployments, thus enhancing the agent's interpretability during execution and fostering trust in autonomous actions taken during defense operations.

Implications and Future Directions

The work provides significant implications for both theoretical advancements in the interpretability of machine learning and practical applications for network cybersecurity. The proposed integration of symbolic reasoning with learning-enabled functionalities in the form of EBTs offers a promising direction for constructing resilient and transparent autonomous systems that can maintain operational effectiveness against evolving threats.

While the approach shows potential, future research could focus on scalability to larger, more complex network topologies and expanding the applicability of these methods to various real-world cyber-defense scenarios. Additionally, evaluating the approach within emulated rather than simulated environments could bridge the gap to commercial deployment, providing insights into its operational viability in real-world systems.

The paper's contribution to the understanding and development of neurosymbolic learning for cybersecurity makes it a valuable reference for designing intelligent, interpretable, and adaptive defense mechanisms in modern network infrastructures.