Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning
Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning
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DOI：
10.1109/access.2022.3165809
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发表时间：
2022-01-01
期刊：
影响因子：
3.9
通讯作者：
Janicke, Helge
中科院分区：
文献类型：
--
作者：
Ferrag, Mohamed Amine;Friha, Othmane;Janicke, Helge
在本文中，我们提出了一种新的物联网和工业物联网应用的综合现实网络安全数据集，称为 Edge-IIoTset，它可以由基于机器学习的入侵检测系统以两种不同的模式使用，即集中式学习和联邦学习。具体来说，该数据集是使用专门构建的 IoT/IIoT 测试台生成的，该测试台具有大量具有代表性的设备、传感器、协议和云/边缘配置。物联网数据由各种物联网设备（超过10种）生成，例如用于传感温度和湿度的低成本数字传感器、超声波传感器、水位检测传感器、pH传感器计、土壤湿度传感器、心率传感器、火焰传感器等）。此外，我们识别并分析了与物联网和工业物联网连接协议相关的 14 种攻击，将其分为五种威胁，包括 DoS/DDoS 攻击、信息收集、中间人攻击、注入攻击和恶意软件攻击。此外，我们还提取了从不同来源（包括警报、系统资源、日志、网络流量）获得的特征，并从 1176 个找到的特征中提出了 61 个具有高相关性的新特征。在处理和分析所提出的现实网络安全数据集之后，我们提供了初步的探索性数据分析，并评估了机器学习方法（即传统机器学习和深度学习）在集中式和联邦式学习模式下的性能。 Edge-IIoTset 数据集可以从 http://ieee-dataport.org/8939 公开访问。
In this paper, 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 dataset has been generated using a purpose-built IoT/IIoT testbed with a large representative set of devices, sensors, protocols and cloud/edge configurations. 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.). Furthermore, 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. In addition, we extract features obtained from different sources, including alerts, system resources, logs, network traffic, and propose new 61 features with high correlations from 1176 found features. 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 (i.e., traditional machine learning as well as deep learning) in both centralized and federated learning modes. The Edge-IIoTset dataset can be publicly accessed from http://ieee-dataport.org/8939.