ToN-IoT

The ToN-IoT dataset is a benchmark that contains diverse real-world scenarios for evaluating intrusion detection systems in heterogeneous Internet of Things networks.

Papers using ToN-IoT (12)

Federated Learning-Enhanced Blockchain Framework for Privacy-Preserving Intrusion Detection in Industrial IoT2025 · 2 citesAdaptive Meta-Aggregation Federated Learning for Intrusion Detection in Heterogeneous Internet of Things2026Lightweight Cluster-Based Federated Learning for Intrusion Detection in Heterogeneous IoT Networks2026CF-HFC:Calibrated Federated based Hardware-aware Fuzzy Clustering for Intrusion Detection in Heterogeneous IoTs2026Collaborative Zone-Adaptive Zero-Day Intrusion Detection for IoBT2026Mist-Assisted Federated Learning for Intrusion Detection in Heterogeneous IoT Networks2025OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoT2025Federated Deep Learning for Intrusion Detection in IoT Networks2023 · 2 citesFederated TON_IoT Windows Datasets for Evaluating AI-based Security Applications2020 · 1 citesData Analytics-enabled Intrusion Detection: Evaluations of ToN_IoT Linux Datasets2020Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges2021Federated PCA on Grassmann Manifold for IoT Anomaly Detection2024