Federated Intrusion Detection System for Distributed Industrial IoT Networks
DOI:
https://doi.org/10.31838/NJAP/08.02.03Keywords:
Federated Learning, Industrial Internet of Things, Intrusion Detection System, Adaptive Aggregation, Edge Intelligence.Abstract
The Distributed Industrial Internet of Things operates in settings where their distributed wireless sensors, edge gateway terminals, and antenna pathways are subject to cybersecurity attacks. This paper describes the author’s original Intelligent Federated Intrusion Detection System (ID-Fed-IDS) that implements Attention-Based Federated Adaptive Aggregation (ABFAA) and operates in extreme distributed industrial applications. Contrary to many federated techniques where the same operation is applied via model averaging, the suggested methodology introduces model averaging in an adaptive, asymmetric fashion such that edge models are assigned attention weights that improve system performance in the presence of heterogeneity in user non-iid (independent, identically distributed) traffic. Each industrial node implements an ultra-low complex hybrid Convolutional Neural Network (CNN) networks and Gated Recurrent Unit (GRU) model to learn the spatial and temporal constructs of the underlying network activity at each node. Privacy is preserved using a secure multiparty computation (SMC) aggregation scheme that avoids transmitting the actual raw data to the model. Notable advancements in the detection of distributed denial of service and stealth probing type attacks, along with operational latency below realtime industrial processing requirements, is achieved. Collectively, the data illustrates that adaptive federated aggregation normalize the detection performance, resiliency, and importantly, the operational scalability of the DIIoT.
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