arXiv:2606.18223v1 Announce Type: cross Abstract: With sophisticated cyber-attacks becoming increasingly prevalent, modern networks require intelligent autonomous cyber-defense agents trained via Reinforcement Learning (RL). These agents employ neurosymbolic approaches such as behavior trees with learning-enabled components (LECs) to learn, reason, adapt, and implement security rules while maintaining critical operations. However, these autonomous networks are partially observable systems, i.e., the cyber-attacker's (red agent's) actions are not observable, making it difficult for the defender
The increasing sophistication of cyber-attacks and the development of advanced AI capabilities are driving the need for autonomous cyber-defense agents.
This research addresses the critical challenge of securing complex digital infrastructure using intelligent autonomous systems, impacting national security and economic stability.
Cyber defense is evolving from human-centric, reactive systems to proactive, AI-driven autonomous agents capable of learning and adapting to 'red agent' tactics.
- · Cybersecurity firms specializing in AI/ML
- · Defense contractors with AI capabilities
- · Organizations with advanced cyber-defense infrastructure
- · Legacy cybersecurity solution providers
- · Adversaries relying on common attack vectors
- · Organizations without robust AI-driven defense
Autonomous cyber-defense agents will enhance the resilience and security of critical network infrastructure.
The proliferation of neurosymbolic AI agents in cyber defense will lead to an 'AI arms race' in the digital domain between defenders and attackers.
This could fundamentally alter the geopolitical balance of power by providing an asymmetric advantage to nations with superior autonomous cyber capabilities.
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Read at arXiv cs.AI