# Federated Mimic Learning for Privacy Preserving Intrusion Detection

arXiv: https://arxiv.org/abs/2012.06974 · HTML: https://mlsanity.com/paper/2012.06974/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2020.

## Built on by (most cited first, 6 of 6)

- [Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions](https://mlsanity.com/paper/2106.09527/) (2021)
- [Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review](https://mlsanity.com/paper/2304.10550/) (2023)
- [Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges](https://mlsanity.com/paper/2108.00974/) (2021)
- [A review of Federated Learning in Intrusion Detection Systems for IoT](https://mlsanity.com/paper/2204.12443/) (2022)
- [GowFed -- A novel Federated Network Intrusion Detection System](https://mlsanity.com/paper/2210.16441/) (2022)
- [Federated Deep Learning for Intrusion Detection in IoT Networks](https://mlsanity.com/paper/2306.02715/) (2023)

## Abstract (arXiv)

Internet of things (IoT) devices are prone to attacks due to the limitation of their privacy and security components. These attacks vary from exploiting backdoors to disrupting the communication network of the devices. Intrusion Detection Systems (IDS) play an essential role in ensuring information privacy and security of IoT devices against these attacks. Recently, deep learning-based IDS techniques are becoming more prominent due to their high classification accuracy. However, conventional deep learning techniques jeopardize user privacy due to the transfer of user data to a centralized server. Federated learning (FL) is a popular privacy-preserving decentralized learning method. FL enables training models locally at the edge devices and transferring local models to a centralized server instead of transferring sensitive data. Nevertheless, FL can suffer from reverse engineering ML attacks that can learn information about the user's data from model. To overcome the problem of reverse engineering, mimic learning is another way to preserve the privacy of ML-based IDS. In mimic learning, a student model is trained with the public dataset, which is labeled with the teacher model that is trained by sensitive user data. In this work, we propose a novel approach that combines the advantages of FL and mimic learning, namely federated mimic learning to create a distributed IDS while minimizing the risk of jeopardizing users' privacy, and benchmark its performance compared to other ML-based IDS techniques using NSL-KDD dataset. Our results show that we can achieve 98.11% detection accuracy with federated mimic learning.