Federated hierarchical MARL for zero-shot cyber defense

Area of Science:

• Cybersecurity and Artificial Intelligence
• Machine Learning for Network Defense
• Distributed Systems Security

Background:

• Traditional cyber defense struggles with evolving, sophisticated threats due to reliance on static, centralized methods.
• Novel and zero-day exploits pose significant challenges to existing detection and response mechanisms.
• Need for adaptive, intelligent systems capable of learning and responding to unseen attack patterns.

Purpose of the Study:

• To present the Adaptive Zero-Shot Hierarchical Multi-Agent Reinforcement Learning (AZH-MARL) framework.
• To enhance cyber defense resilience through hierarchical task decomposition and zero-shot learning.
• To enable collaborative defense across domains via privacy-preserving federated learning.

Main Methods:

• Implemented a hierarchical reinforcement learning structure for task decomposition and agent coordination.
• Integrated zero-shot learning with semantic mapping for recognizing and responding to novel threats.
• Utilized federated learning for secure, cross-domain knowledge sharing without compromising data privacy.

Main Results:

• Achieved high detection rates: 94.2% for known attacks, 82.7% for zero-day exploits, with a 3.8% false positive rate.
• Demonstrated strong performance against Advanced Persistent Threats (APTs) with an 87.3% containment rate.
• Reduced mean response time by 35% (known) and 42% (zero-day); decreased communication overhead by 45% via federated learning.

Conclusions:

• The AZH-MARL framework offers a significant advancement in adaptive and resilient cyber defense.
• Zero-shot learning and federated knowledge sharing are crucial for handling novel threats and distributed environments.
• The proposed approach establishes a new standard for robust cybersecurity against complex, evolving threats.