ToN_IoT datasets

Collecting and analysing heterogeneous data sources from the Internet of Things (IoT) and Industrial IoT (IIoT) are essential for training and validating the fidelity of cybersecurity applications-based machine learning. However, the analysis of those data sources is still a big challenge for reducing high dimensional space and selecting important features and observations from different data sources. The study proposes a new testbed for an IIoT network that was utilised for creating new datasets called TON_IoT that collected Telemetry data, Operating systems data and Network data. The testbed is deployed using multiple virtual machines including hosts of windows, Linux and Kali Linux operating systems to manage the interconnections between the three layers of IIoT, Cloud and Edge/Fog systems. The initial statistical evaluation of the datasets reveals their capability for evaluating cybersecurity applications such as intrusion detection, threat intelligence, adversarial machine learning and privacy-preserving models.

收集并分析来自物联网（IoT）与工业物联网（IIoT）的异构数据源，对于训练与验证基于机器学习的网络安全应用的保真度至关重要。然而，对这类数据源进行分析仍存在显著挑战：如何降低高维空间维度，并从不同数据源中筛选关键特征与观测样本。本研究提出了一种面向IIoT网络的新型测试床，用于构建名为TON_IoT的数据集，该数据集涵盖遥测数据、操作系统数据与网络数据三类内容。该测试床依托多台虚拟机部署，搭载Windows、Linux及Kali Linux操作系统主机，用于实现IIoT、云计算与边缘/雾计算三层架构间的互联互通。对该数据集的初步统计评估显示，其可用于评估入侵检测、威胁情报、对抗机器学习以及隐私保护模型等各类网络安全应用。

搜集汇总

背景与挑战

背景概述

ToN_IoT数据集是一个专为物联网和工业物联网设计的异构数据集，包含遥测、操作系统和网络数据，用于评估基于AI的网络安全应用。数据集通过模拟复杂工业环境收集，支持入侵检测和隐私保护等多种安全研究。