CIDS-Sim: Simulator for CIDS based on Federated Learning
CIDS-Sim (Collaborative Intrusion Detection System - Simulator) is a sophisticated simulation platform engineered to replicate, analyze, and optimize CIDS using federated learning methodologies. Tailored to address modern cybersecurity challenges, the software supports academic research, technological development, and educational initiatives by simulating realistic, decentralized environments characterized by non-independent and identically distributed (non-IID) data and heterogeneous data distributions. These scenarios mirror the complexity of real-world networks, enabling robust testing and validation of CIDS under conditions that reflect actual operational challenges, such as uneven data quality, varying attack patterns, and device diversity.
At its core, CIDS-Sim employs a centralized federated learning architecture that harmonizes privacy preservation with collaborative intelligence. By design, sensitive data remains localized on distributed client nodes—such as edge devices, servers, or IoT systems—while global machine learning models are iteratively refined through secure parameter aggregation. This approach not only safeguards data confidentiality but also enables organizations to leverage collective insights without compromising proprietary or personal information.
Github: https://github.com/aulwardana/CIDS-Sim
The simulator is open-source and free to use for researchers, practitioners in the industry, and educators. If you use this software, please provide citation credit to our publication available at this link.
User Guide