ADAPTIVE CYBER DEFENSE IN THE ERA OF PLATFORMIZATION: INTEGRATING REINFORCEMENT LEARNING AND FEDERATED ARCHITECTURES FOR REAL-TIME THREAT MITIGATION
Keywords:
Cybersecurity Platformization, Reinforcement Learning, Federated Learning, Adaptive Security ProtocolsAbstract
Background: The rapid expansion of digital ecosystems into cloud computing and Internet of Things (IoT) environments has rendered traditional, perimeter-based security models obsolete. As cyber threats evolve into automated, AI-driven campaigns, enterprise security must shift toward "platformization"—the consolidation of security tools into unified, data-centric architectures.
Objective: This study proposes a novel, hybrid security
framework: the Federated Adaptive Defense Platform (FADP). The objective is to integrate Reinforcement Learning (RL) for real-time, adaptive protocol generation with Federated Learning (FL) to ensure privacy-preserving threat intelligence sharing across decentralized networks.
Methods: We designed a multi-agent RL system capable of modifying security protocols autonomously in response to detected anomalies. This was coupled with a Federated Learning architecture to aggregate threat models from edge devices (such as VANETs and industrial controllers) without centralizing sensitive raw data. The system was tested against diverse attack vectors, including DDoS, adversarial evasion, and false data injection.
Results: The FADP demonstrated a 94.3% detection rate for previously unknown zero-day attacks, significantly outperforming static machine learning models. Furthermore, the RL agent reduced incident response latency by 40% compared to human-in-the-loop workflows.
Conclusion: The integration of RL and FL within a platformized security architecture offers a robust solution for modern cyber defense. This approach not only enhances real-time detection capabilities but also addresses critical data privacy concerns, paving the way for resilient, autonomous security ecosystems in the 6G era.
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