Dataset For Detection In Multi-IDS Environment

The dataset presented aims to support research in developing robust Intrusion Detection Systems (IDS) for modern networks. It simulates a network environment of a fictitious organization with multiple vulnerable hosts and strategic IDS deployments. The experimental setup uses virtual machines to emulate an attacker machine, vulnerable hosts, and IDS devices, connected via Open vSwitches (OVS) with port mirroring to capture traffic. Attack scenarios include multi-hop attacks targeting internal hosts by exploiting vulnerabilities and bypassing traffic restrictions. The raw PcapNG files are complemented with extracted features in CSV format, supporting Machine Learning (ML) analysis. The dataset is designed for training and evaluating IDS models capable of detecting complex, multi-stage attacks in realistic network environments.