IoT Emulated Dataset for ICMP/Ping Normal and Malicious Traffic

These datasets are related to Intrusion Detection System, Computer Network Traffic and IoT.

These datasets are generated for the purpose of differentiating ICMP/Ping normal and malicious traffic that are generated from an embedded device (IoT). The differentiation analysis is done using machine learning.

There are three types of files that depend on each module of our research framework. The data generation sequence is as follows:

The pcap files (network traffic)  are generated first, the device used to generate the data is an ESP-01s. Afterwards, the pcap files are transformed into log files using the Zeek tool, the log files are then extracted and placed into CSV files.

The CSV files are labeled and ready for the Machine Learning process.

The publication reference for this work is here : https://doi.org/10.1109/ACCESS.2023.3327061

The code link: https://zenodo.org/badge/latestdoi/619245496

This version of the release (0.2.0) is for ping flood with spoofed IPs, however, the previous version (0.1.0) is for static IP

本数据集与入侵检测系统（Intrusion Detection System）、计算机网络流量及物联网（IoT）相关。

本数据集旨在区分由嵌入式设备（IoT）生成的ICMP/Ping正常流量与恶意流量，相关区分分析通过机器学习完成。

本数据集包含三类文件，分别对应研究框架的各个模块，数据生成流程如下：

首先生成pcap文件（网络流量），数据采集设备为ESP-01s。随后通过Zeek工具将pcap文件转换为日志文件，再将日志文件提取并整理为CSV格式文件。

所得CSV文件均已完成标注，可直接用于机器学习流程。

本研究的公开引用信息如下：https://doi.org/10.1109/ACCESS.2023.3327061

代码仓库链接：https://zenodo.org/badge/latestdoi/619245496

本次发布的版本（0.2.0）针对IP地址欺骗的ping泛洪攻击，而上一版本（0.1.0）则针对静态IP场景。