Beyond Rewards in Reinforcement Learning for Cyber Defence

2025-01-31 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

This research evaluates the impact of reward function design on deep reinforcement learning (DRL) agents for autonomous cyber defense (ACD). Using two established cyber gyms, Yawning Titan and miniCAGE, across network sizes from 2 to 50 nodes, various action spaces, and both PPO and DQN algorithms, the study compared sparse and dense reward functions. A novel ground truth scoring mechanism, Score_GT, was introduced to accurately measure compromised nodes, including intra-step events, independent of the reward function. Results from 25 independent runs and 1000 evaluation episodes, requiring 720 processor days, demonstrate that sparse positive-negative (SPN) and sparse positive (SP) reward functions consistently yield more effective and reliable cyber defense agents with lower-risk policies. Dense rewards, common in current cyber gyms, were found to constrain agent performance, increase risks, and reduce training reproducibility.

Key takeaway

For AI Security Engineers developing autonomous cyber defense agents, you should critically re-evaluate the reward functions used in your DRL training environments. Opt for sparse, goal-aligned reward structures like SPN or SP, as they demonstrably lead to more effective, reliable, and lower-risk defense policies, especially in dynamic attacker scenarios. Additionally, integrate ground truth evaluation methods to accurately assess agent performance beyond standard episodic rewards, ensuring your agents are robust against real-world attack complexities.

Key insights

Sparse, goal-aligned reward functions enhance DRL cyber defense agent effectiveness, reliability, and risk profiles.

Principles

• Dense rewards bias DRL agents towards suboptimal, riskier solutions.
• Sparse rewards, if goal-aligned and frequent, improve training reliability.
• Reward function design critically impacts policy performance and risk.

Method

A ground truth score (Score_GT) measures maximum compromised nodes (intra- and end-step), enabling robust comparison of reward functions. Reliability is quantified by DT and DR', and risk by RF_alpha (CVaR).

In practice

• Prioritize sparse reward functions for DRL cyber defense training.
• Implement ground truth evaluation to assess true agent performance.
• Consider agent order variability in cyber gym simulations.

Topics

• Reinforcement Learning
• Cyber Defense
• Reward Function Design
• Autonomous Agents
• Cyber Gyms
• Network Security

Code references

• microsoft/cyberbattlesim
• cage-challenge/CybORG

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.