FetFIDS: A Feature Embedding Attention based Federated Network Intrusion Detection Algorithm

The nodes represent the devices chosen to participate in federated training and the local models are the models used on the devices to train with their local, private data.Figure 1: Architecture of the proposed intrusion detection system. A. Nodes We consider a network of Internet of Things devices connected to each other. Each node monitors the data packets that are flowing through the network and detect malicious data packets injected by attackers using a deep learning based intrusion detection model deployed on the local devices. The intrusion detection systems are always collecting local data, training their models on local data and participating in communication rounds with the central server. B. Attention Based Deep Learning Model The deep learning model is an attention based model, presented in Figure 1. The input is a 1D vector that contains the features of the network traffic data. Attention models are typically aimed at drawing the focus of the model on the contextually important parts of an input. The multihead attention function consists of several attention heads that focus the attention of the model on different aspects or different features of the data. The computed attention matrix tells the model which part of the input is important in relation to itself.