FetFIDS: A Feature Embedding Attention based Federated Network Intrusion Detection Algorithm
This work addresses network security for edge deployments by enhancing federated intrusion detection, though it appears incremental as it modifies an existing transformer approach.
The paper tackled improving intrusion detection in federated learning environments by using feature embedding instead of positional embedding in a transformer-based system, resulting in FetFIDS outperforming state-of-the-art methods.
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.