Mininet-IDS: A Step Towards Reproducible Research for Machine Learning Based Intrusion Detection Systems

Software-Defined Networking (SDN) has revolutionized network management by enabling more flexible, programmable, and controlled networks. However, the SDN controller can be a target for attacks that could bring down the entire network. In this context, intrusion detection systems (IDS) are essential for maintaining network security. Modern IDS are often enhanced with machine learning models to detect a range of network attacks. This process typically includes dataset preprocessing, model training, and integration of these models into network emulators like Mininet. However, this workflow can be complex and error-prone. To address these challenges, we present Mininet-IDS, a comprehensive command-line interface (CLI) tool that streamlines the process by offering integrated functionalities for dataset preprocessing, feature selection, model training, and deployment within the Mininet environment. Our tool simplifies the workflow by eliminating compatibility issues and ensuring reproducibility. We evaluate Mininet-IDS using the NSL-KDD dataset, training various machine learning models to detect DDoS attacks. Our results demonstrate the tool's efficiency and accuracy, making it a valuable resource for network security researchers to conduct experiments with minimal machine learning expertise.