Discretization-based ensemble model for robust learning in IoT

TL;DR

This paper proposes a discretization-based ensemble stacking method to enhance the robustness of machine learning models in IoT device identification against adversarial attacks, improving security and reliability.

Contribution

It introduces a novel combination of discretization and ensemble techniques to strengthen ML model security in IoT device identification.

Findings

The proposed method improves robustness against white-box attacks.

The model maintains high accuracy under black-box attack scenarios.

Experimental results on real-world data validate the approach's effectiveness.

Abstract

IoT device identification is the process of recognizing and verifying connected IoT devices to the network. This is an essential process for ensuring that only authorized devices can access the network, and it is necessary for network management and maintenance. In recent years, machine learning models have been used widely for automating the process of identifying devices in the network. However, these models are vulnerable to adversarial attacks that can compromise their accuracy and effectiveness. To better secure device identification models, discretization techniques enable reduction in the sensitivity of machine learning models to adversarial attacks contributing to the stability and reliability of the model. On the other hand, Ensemble methods combine multiple heterogeneous models to reduce the impact of remaining noise or errors in the model. Therefore, in this paper, we integrate discretization techniques and ensemble methods and examine it on model robustness against adversarial attacks. In other words, we propose a discretization-based ensemble stacking technique to improve the security of our ML models. We evaluate the performance of different ML-based IoT device identification models against white box and black box attacks using a real-world dataset comprised of network traffic from 28 IoT devices. We demonstrate that the proposed method enables robustness to the models for IoT device identification.