ToN IoT datasets

The ToN IoT datasets consist of heterogeneous data collected from IoT telemetry, operating systems (Windows and Linux), and network traffic, and are used to evaluate the performance of AI-enabled security applications.

Papers using ToN IoT datasets (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 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