ToN-IoT

The 'ToN-IoT' dataset is a benchmark that contains network traffic data specifically designed for evaluating intrusion detection systems in Internet of Things (IoT) environments.

Papers using ToN-IoT (13)

Mist-Assisted Federated Learning for Intrusion Detection in Heterogeneous IoT Networks2025 · 2 citesFederated Learning-Enhanced Blockchain Framework for Privacy-Preserving Intrusion Detection in Industrial IoT2025 · 2 citesAoI-Guided Client Selection for Robust and Timely Federated Intrusion Detection in Cloud-Edge Security Analytics2026Adaptive 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 IoBT2026OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoT2025Federated PCA on Grassmann Manifold for IoT Anomaly Detection2024 · 33 citesFederated 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 Challenges2021