MQTT-IoT-IDS2020: MQTT Internet of Things Intrusion Detection Dataset

Message Queuing Telemetry Transport (MQTT) protocol is one of the most used standards used in Internet of Things (IoT) machine to machine communication. The increase in the number of available IoT devices and used protocols reinforce the need for new and robust Intrusion Detection Systems (IDS). However, building IoT IDS requires the availability of datasets to process, train and evaluate these models. The dataset presented in this paper is the first to simulate an MQTT-based network. The dataset is generated using a simulated MQTT network architecture. The network comprises twelve sensors, a broker, a simulated camera, and an attacker. Five scenarios are recorded: (1) normal operation, (2) aggressive scan, (3) UDP scan, (4) Sparta SSH brute-force, and (5) MQTT brute-force attack. The raw pcap files are saved, then features are extracted. Three abstraction levels of features are extracted from the raw pcap files: (a) packet features, (b) Unidirectional flow features and (c) Bidirectional flow features. The csv feature files in the dataset are suited for Machine Learning (ML) usage. Also, the raw pcap files are suitable for the deeper analysis of MQTT IoT networks communication and the associated attacks.

搜集汇总

背景与挑战

背景概述

MQTT-IoT-IDS2020是首个模拟基于MQTT协议的物联网入侵检测数据集，专为训练和评估物联网入侵检测系统而设计。它通过模拟网络架构生成，包含正常操作和四种攻击场景（如暴力破解和扫描攻击），并提取了数据包、单向流和双向流三个层次的特征，提供原始pcap文件和CSV特征文件，既支持机器学习应用，也便于深入分析MQTT网络通信与安全威胁。