# GowFed -- A novel Federated Network Intrusion Detection System

arXiv: https://arxiv.org/abs/2210.16441 · HTML: https://mlsanity.com/paper/2210.16441/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 47 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [D"IoT: A Federated Self-learning Anomaly Detection System for IoT](https://mlsanity.com/paper/1804.07474/) (2018)
- [Decentralized Collaborative Learning of Personalized Models over Networks](https://mlsanity.com/paper/1610.05202/) (2016)
- [Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach](https://mlsanity.com/paper/2007.09712/) (2020)
- [Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process](https://mlsanity.com/paper/2001.09697/) (2020)
- [Federated Mimic Learning for Privacy Preserving Intrusion Detection](https://mlsanity.com/paper/2012.06974/) (2020)
- [A review of Federated Learning in Intrusion Detection Systems for IoT](https://mlsanity.com/paper/2204.12443/) (2022)
- [Mimic Learning to Generate a Shareable Network Intrusion Detection Model](https://mlsanity.com/paper/1905.00919/) (2019)

## Built on by (most cited first, 2 of 2)

- [usfAD Based Effective Unknown Attack Detection Focused IDS Framework](https://mlsanity.com/paper/2403.11180/) (2024)
- [Celtibero: Robust Layered Aggregation for Federated Learning](https://mlsanity.com/paper/2408.14240/) (2024)

## Abstract (arXiv)

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.