Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications: Centralized and Federated Learning

In this project, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning. Specifically, the proposed testbed is organized into seven layers, including, Cloud Computing Layer, Network Functions Virtualization Layer, Blockchain Network Layer, Fog Computing Layer, Software-Defined Networking Layer, Edge Computing Layer, and IoT and IIoT Perception Layer. In each layer, we propose new emerging technologies that satisfy the key requirements of IoT and IIoT applications, such as, ThingsBoard IoT platform, OPNFV platform, Hyperledger Sawtooth, Digital twin, ONOS SDN controller, Mosquitto MQTT brokers, Modbus TCP/IP, ...etc. The IoT data are generated from various IoT devices (more than 10 types) such as Low-cost digital sensors for sensing temperature and humidity, Ultrasonic sensor, Water level detection sensor, pH Sensor Meter, Soil Moisture sensor, Heart Rate Sensor, Flame Sensor, ...etc.). However, we identify and analyze fourteen attacks related to IoT and IIoT connectivity protocols, which are categorized into five threats, including, DoS/DDoS attacks, Information gathering, Man in the middle attacks, Injection attacks, and Malware attacks. After processing and analyzing the proposed realistic cyber security dataset, we provide a primary exploratory data analysis and evaluate the performance of machine learning approaches in both centralized and federated learning modes. 

本研究提出了一款面向物联网（IoT）与工业物联网（IIoT）应用的新型综合真实网络安全数据集，命名为Edge-IIoTset。该数据集可被基于机器学习（machine learning）的入侵检测系统以两种不同模式使用，即集中式学习（centralized learning）与联邦学习（federated learning）模式。具体而言，本研究搭建的实验测试床共分为七层，分别为云计算层、网络功能虚拟化层、区块链网络层、雾计算层、软件定义网络层、边缘计算层以及物联网与工业物联网感知层。针对各层的物联网与工业物联网应用核心需求，本研究采用了多款新兴技术，包括ThingsBoard物联网平台、OPNFV平台、Hyperledger Sawtooth、数字孪生（Digital twin）、ONOS SDN控制器（ONOS SDN controller）、Mosquitto MQTT消息代理（Mosquitto MQTT brokers）、Modbus TCP/IP协议等。数据集的物联网数据由十余种不同类型的物联网设备生成，包括用于温湿度采集的低成本数字传感器、超声波传感器、水位检测传感器、pH传感器、土壤湿度传感器、心率传感器、火焰传感器等。此外，本研究识别并分析了14种针对物联网与工业物联网连接协议的攻击，并将其划分为五大威胁类别，即拒绝服务/分布式拒绝服务（DoS/DDoS）攻击、信息收集攻击、中间人攻击（Man in the middle attacks）、注入攻击以及恶意软件攻击。在对该真实网络安全数据集进行处理与分析后，本研究开展了初步的探索性数据分析（exploratory data analysis），并针对集中式学习与联邦学习两种模式下的机器学习方法性能进行了评估。

搜集汇总

背景与挑战

背景概述

Edge-IIoTset是一个全面的IoT和IIoT网络安全数据集，包含多种攻击类型和正常流量数据，支持集中式和联邦学习模式。数据集格式包括CSV、PCAP等，适用于机器学习和深度学习研究。