  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.
