.. _future: Future Research =========== There are some idea to develop future research using this simulator. The design of the heterogeneous network scenario, which assigns distinct datasets to individual nodes to emulate the diversity of real-world network environments, draws methodological inspiration from the framework proposed in research **[1]**. Similarly, the non-IID (non-independent and identically distributed) data scenario in this work builds upon the foundational frameworks established in research **[2]**, particularly in simulating random data distributions and client-specific feature variability. Synthesizing principles from these studies, the developed simulator serves as a core component of the experimental framework presented in research **[3]**. By integrating the CoAt-Set dataset from research **[4]**—which provides labeled attack patterns and heterogeneous traffic profiles—and incorporating theoretical advancements from federated learning literature in research **[5]**, this simulator is engineered to address critical challenges in collaborative intrusion detection. Specifically, it enables researchers and practitioners to rigorously test federated learning-based CIDS architectures under realistic, decentralized conditions characterized by non-IID data and heterogeneous network topologies. For educators, the tool offers a pedagogical platform to demonstrate the interplay between data privacy, model convergence, and threat detection efficacy, thereby bridging theoretical concepts with practical implementation in cybersecurity education. **References** 1. Aulia Arif Wardana, Grzegorz Kołaczek, Arkadiusz Warzynski, and Parman Sukarno. Collaborative intrusion detection using weighted ensemble averaging deep neural network for coordinated attack detection in heterogeneous network. Int. J. Inf. Secur., 23(5):3329–3349, October 2024. `link to publication `_ 2. Aulia Arif Wardana, Grzegorz Kołaczek, and Parman Sukarno. Lightweight, trust-managing, and privacy-preserving collaborative intrusion detection for internet of things. Appl. Sci. (Basel), 14(10):4109, May 2024. `link to publication `_ 3. On Going 4. Aulia Arif Wardana, Grzegorz Kołaczek, and Parman Sukarno. Coat-set: Transformed coordinated attack dataset for collaborative intrusion detection simulation. Data in Brief, page 111354, 2025. `link to publication `_ 5. Aulia Arif Wardana and Parman Sukarno. Taxonomy and survey of collaborative intrusion detection system using federated learning. ACM Comput. Surv., October 2024. `link to publication `_