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.

通过集成离散化技术和集成方法，我们提出了一种基于离散化的集成堆叠技术，以提高我们的机器学习模型的安全性，通过对 28 个物联网设备网络流量的真实数据集进行白盒和黑盒攻击的性能评估，我们证明了提出的方法使得物联网设备识别模型具有鲁棒性。

基于离散化的物联网稳健学习集成模型

Discretization-based ensemble model for robust learning in IoT

While the use of the Internet of Things is becoming more and more popular,
many security vulnerabilities are emerging with the large number of devices
being introduced to the market. In this environment, IoT device identification
methods provide a preventive security measure as an important factor in
identifying these devices and detecting the vulnerabilities they suffer from.
In this study, we present a method that identifies devices in the Aalto dataset
using Long short-term memory (LSTM)

本研究提出了一种利用长短期记忆（LSTM）来识别 Aalto 数据集中的设备的方法，该方法为预防性安全措施提供了一种重要手段以识别这些设备并检测它们遭受的漏洞。