Towards Adaptive Cybersecurity: Implementing FedIDFR in Kubernetes for Enhanced Protection

The Industrial Internet of Things (IIoT) has significantly enhanced agility and productivity in the industrial sectors by interconnecting smart sensors and actuators. However, this increased connectivity also broadens the attack surface and exposes traditionally isolated devices to evolving cybersecurity threats. Addressing this need, this paper facilitates a novel integration of Kubernetes' flexible container orchestration with the next-generation, learning-based firewall techniques, introducing the Federated Incremental Learning-based Network Intrusion Detection Systems and Dynamic Firewall Rule management (FedIDFR) framework. The FedIDFR framework harnesses Kubernetes' container orchestration capabilities to enhance the flexibility, scalability, and responsiveness of security services, providing a robust solution to the evolving industrial security needs. For the dynamic firewall management, we employ a federated incremental learning algorithm to enable real-time and adaptive security responses to a wide variety of threats. This incremental approach surpasses the static dataset limitations while preserving industrial business privacy. Comprehensive experiments demonstrate a significant 24.0% improvement in response times over traditional deployments, illustrating the benefits of our approach in the dynamic networked industrial systems. These results also establish a new benchmark for adaptive and resilient network security architectures, particularly in settings demanding high flexibility and rapid response to the emerging threats.