Adaptive Threat Intelligence through Federated Learning for Secure Distributed Network Environments with Heterogeneous Nodes
Keywords:
Federated Learning, Threat Intelligence, Cybersecurity, Distributed Networks, Heterogeneous Nodes, Anomaly Detection, Edge ComputingAbstract
The rise of distributed networks with heterogeneous devices has brought increased vulnerability to cyber-attacks, particularly in environments where data centralization is infeasible due to privacy or infrastructure constraints. This paper proposes a Federated Learning (FL) approach to enable adaptive threat intelligence across decentralized, multi-node systems. Our framework leverages collaborative learning among heterogeneous nodes to detect threats in real-time without sharing raw data. We evaluate the system across multiple threat categories, demonstrating an increase of 13.7% in detection accuracy over traditional centralized systems while reducing response latency by 22%. The proposed model exhibits resilience to data and device heterogeneity, offering a secure, scalable solution for modern cyber-defense architecture.
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Copyright (c) 2022 Carlos González (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
