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

TL;DR

FetFIDS introduces a federated deep learning approach using feature embedding and attention mechanisms to enhance network intrusion detection while preserving privacy in edge learning scenarios.

Contribution

The paper proposes a novel transformer-based federated intrusion detection system utilizing feature embedding instead of positional embedding for improved performance.

Findings

FetFIDS outperforms existing intrusion detection systems in federated settings.

The model demonstrates high suitability for federated learning environments.

Code is publicly available for reproducibility.

Abstract

Intrusion Detection Systems (IDS) have an increasingly important role in preventing exploitation of network vulnerabilities by malicious actors. Recent deep learning based developments have resulted in significant improvements in the performance of IDS systems. In this paper, we present FetFIDS, where we explore the employment of feature embedding instead of positional embedding to improve intrusion detection performance of a transformer based deep learning system. Our model is developed with the aim of deployments in edge learning scenarios, where federated learning over multiple communication rounds can ensure both privacy and localized performance improvements. FetFIDS outperforms multiple state-of-the-art intrusion detection systems in a federated environment and demonstrates a high degree of suitability to federated learning. The code for this work can be found at https://github.com/ghosh64/fetfids.