A Scalable Intrusion Detection Approach for Industrial Internet of Things Based on Federated Learning and Attention Mechanism
Mudhafar Nuaimi, Lamia Chaari Fourati, Bassem Ben Hamed · 2023
The Industrial Internet of Things (IIoT) widespread adoption has prompted multiple breaches on IIoT devices by attackers. Thus this threatens the security of data for the end user. Recurrent neural networks (RNNs) have been used for intruder detection in IIoT because IIoT traffics are created consecutively, but they are unable to represent long traffic sequences and cannot be parallelized either. In order to solve these problems in this paper we have introduced the Attention technique which is applied to the encoding layer. We also used a federated learning (FL) approach to reduce the communication overhead of collecting data from each worker node and storing it in the cloud server in the case of a centralized model, thus preserving network scalability. With the use of the Edge-IIoT dataset, we test our suggested methodology. The outcomes of our FL experiment enable the system to scale.