Abstract:
In the rapidly-developing Internet of Things (IoT) ecosystem, safeguarding the privacy and
accuracy of linked devices and networks is of utmost importance, with the challenge lying
in effective implementation of Intrusion Detection Systems (IDS) on resource-constrained
IoT devices. This study introduces a Differential Privacy-Aided Federated Learning
architecture for intrusion detection in IoT contexts as a novel approach to addressing
these difficulties. To build an intrusion detection model, we combined components of
a Convolutional Neural Network (CNN) with Bidirectional Long Short-Term Memory
(BiLSTM). We apply this approach to the Bot-IoT dataset, which was rigorously curated
by the University of New South Wales (UNSW) and N-BaIoT dataset. Our major goal is
to create a model that delivers high accuracy while protecting privacy, an often overlooked
aspect of IoT security. Intrusion detection tasks are distributed across multiple IoT devices
using federated learning principles to protect data privacy, incorporating the differential
privacy framework to gauge and minimize information leakage, all while investigating
the intricate relationship between privacy and accuracy in pursuit of an ideal compromise.
We implemented two aggregation methods, namely, Federated Averaging (FedAvg) and
Federated Proximal (FedProx). The trade-off between privacy preservation and model
accuracy is investigated by adjusting the privacy loss and noise multiplier. Our research
enhances IoT security by introducing a deep learning model for intrusion detection in IoT
devices, explores the integration of differential privacy in federated learning framework for
IoT and offers guidance on minimizing the accuracy- privacy trade-off based on specific
privacy and security needs. Our study explores the privacy-accuracy trade-off by examining
the effects of varying epsilon values on accuracy for various delta values for a range of
clients between 5 to 25. We also investigate the influence of several noise multipliers on
accuracy and find a consistent accuracy curve, especially around a noise multiplier value
of about 0.5 for both the aggregation methods and with a higher accuracy for FedProx.
The findings of this study have the possibilities to enhance IoT ecosystem security and
privacy, contributing to the IoT landscape’s trustworthiness and sustainability.
