Privacy-Preserving Intrusion Detection in Industrial IoT Ecosystems via Decentralized Federated Learning with Proximal Regularization
TL;DR
A privacy-preserving, robust, localized deep learning-based IDS, which utilizes Federated Proximal (FedProx) optimization and incorporates parameterized proximal regularization term (mu = 0.5) as a part of local loss function to penalize client parameters deviation and eliminate client drift phenomenon is suggested.
Abstract
The rapid development of the Industrial Internet of Things (IIoT) has transformed the current industrial control systems (ICS), but in the process has revealed operational technology (OT) to advanced cyber-threats. Conventional centralized intrusion detection systems (IDS) demand the pooling of massive telemetry traffic, a requirement that contributes to prohibitive communication latencies and breaches strict corporate data privacy requirements. Although Federated Learning (FL) provides a more decentralized alternative by training the models locally on edge gateways, it performs badly in non-Identically and Independently Distributed (non-IID) network traffic a condition ubiquitous in heterogeneous factory setting. The paper suggests a privacy-preserving, robust, localized deep learning-based IDS, which utilizes Federated Proximal (FedProx) optimization. The framework is based on multi-layer feed-forward neural network architecture, only that it incorporates parameterized proximal regularization term (mu = 0.5) as a part of local loss function to penalize client parameters deviation and eliminate client drift phenomenon.