"Offsim_Dataset"

"This dataset, named OffSim, has been specifically designed to support research on fraud detection in offline Central Bank Digital Currency (CBDC) payment systems. OffSim is a synthetic, large-scale dataset generated using a custom multi-agent simulator that extends the widely used PaySim framework. While PaySim replicates transactional behavior in mobile money systems such as M-Pesa, OffSim introduces novel features tailored to offline transaction scenarios, including synchronization delay, peer-to-peer indicators, merchant trust scores, and simulated fraud vectors such as double spending and timestamp tampering.The dataset reflects the operational constraints of offline environments, such as limited connectivity, device-level storage, and delayed ledger reconciliation. It provides labeled examples of both legitimate and fraudulent transactions, enabling robust benchmarking of fraud detection models. The dataset is ideal for training and evaluating hybrid detection architectures that combine rule-based systems with machine learning techniques, especially under extreme class imbalance and resource-constrained deployment settings.OffSim promotes reproducible research in secure digital payments and supports the development of explainable and efficient fraud detection algorithms tailored to emerging CBDC infrastructure"