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

Message Queuing Telemetry Transport (MQTT) protocol is one of the most recent 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 and 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

该数据集是首个专注于MQTT物联网协议的入侵检测数据集，模拟了包含正常操作和四种攻击类型（如暴力破解和扫描）的网络场景。它提供了原始数据包（pcap）和多层次特征（数据包、单向流、双向流）的CSV文件，专门设计用于机器学习模型的训练和评估，以支持物联网安全研究。