CIDS-Sim: Simulator for collaborative intrusion detection system based on federated learning

This research introduces CIDS-Sim, a simulator for Collaborative Intrusion Detection Systems (CIDS) based on federated learning, addressing the complexity of coordinated attacks on networks. Traditional Intrusion Detection Systems (IDS) are limited by isolated operations and privacy concerns. CIDS-Sim leverages federated learning to maintain data privacy while enabling collaborative anomaly detection. It assesses collaboration strategies, federated learning’s privacy-performance trade-offs, and different attack vectors and defenses. CIDS-Sim is a critical tool for researchers and practitioners to develop secure IDS solutions, offering a robust platform for simulating and evaluating the dynamics of collaborative defense strategies. CIDS-Sim is also suitable for educators or lecturers who want to teach the concept of CIDS.