GowFed -- A novel Federated Network Intrusion Detection System

TL;DR

GowFed introduces a federated learning-based network intrusion detection system that leverages Gower Dissimilarity matrices and attention mechanisms to enhance privacy-preserving threat detection in industrial networks.

Contribution

This paper presents GowFed, a novel federated intrusion detection system combining Gower Dissimilarity and federated averaging, including variants with attention mechanisms, tested via TensorFlow Federated.

Findings

GowFed effectively detects network threats without exposing sensitive data.

Variants with attention mechanisms improve detection accuracy.

Federated approach reduces communication overhead compared to centralized systems.

Abstract

Network intrusion detection systems are evolving into intelligent systems that perform data analysis while searching for anomalies in their environment. Indeed, the development of deep learning techniques paved the way to build more complex and effective threat detection models. However, training those models may be computationally infeasible in most Edge or IoT devices. Current approaches rely on powerful centralized servers that receive data from all their parties - violating basic privacy constraints and substantially affecting response times and operational costs due to the huge communication overheads. To mitigate these issues, Federated Learning emerged as a promising approach, where different agents collaboratively train a shared model, without exposing training data to others or requiring a compute-intensive centralized infrastructure. This work presents GowFed, a novel network threat detection system that combines the usage of Gower Dissimilarity matrices and Federated averaging. Different approaches of GowFed have been developed based on state-of the-art knowledge: (1) a vanilla version; and (2) a version instrumented with an attention mechanism. Furthermore, each variant has been tested using simulation oriented tools provided by TensorFlow Federated framework. In the same way, a centralized analogous development of the Federated systems is carried out to explore their differences in terms of scalability and performance - across a set of designed experiments/scenarios. Overall, GowFed intends to be the first stepping stone towards the combined usage of Federated Learning and Gower Dissimilarity matrices to detect network threats in industrial-level networks.