[UPDATE] For a more detailed approach, take a look in a proposed framework for end-to-end Network Intrusion Detection Systems (dataset generation, ML model, performance evaluation and deployment) take a look on the AB-TRAP framework repository!

This repository is composed by a Dataset of Probing Attacks (Port Scan) performed with nmap, unicornscan, hping3, zmap and masscan.

It also presents a way to extract background traffic to be used as "normal" traffic to support Machine Learning algorithms development in IDS research. In this project the current source are the MAWILab datasets.

Contains all VMs infrastructure to generate the malicious dataset.

Contains all scripts to generate the normal packets dataset.

Fontugne, R., Borgnat, P., Abry, P., & Fukuda, K. (2010, November). Mawilab: combining diverse anomaly detectors for automated anomaly labeling and performance benchmarking. In Proceedings of the 6th International Conference (p. 8). ACM.

Gustavo de Carvalho Bertoli, Lourenço Alves Pereira Júnior, Filipe Alves Neto Verri, & Osamu Saotome. (2019). Probing Dataset (Version v1.0.0) [Data set]. Zenodo. http:https://doi.org/10.5281/zenodo.3558350