A real-world cybersecurity dataset capturing MQTT-based IoT network traffic with live attacks and anomalous behavior. Collected from an active deployment with multiple attack types including DoS, SlowITe, and malformed injections. Provides both raw and preprocessed CSV files with rich metadata for intrusion detection and anomaly classification research.
The Industrial IoT Dataset (Synthetic) provides a large-scale simulation of sensor readings and operational metrics from machines deployed in a smart factory environment. It focuses on predictive maintenance and anomaly detection.
IMAD-DS captures multi-rate, multi-sensor signals from scaled industrial machines, including a robotic arm and a brushless motor, for anomaly detection research.
The Gotham Dataset is a large-scale, reproducible benchmark for evaluating decentralized Intrusion Detection Systems (IDS) and Federated Learning in virtualized smart cities. It captures interface-level network traffic from 78 heterogeneous IoT devices, including complex attack vectors like Mirai botnets, Merlin C2 traffic, and CoAP amplification, preserving the non-IID nature of edge data for realistic AI security training.
This Industrial IoT dataset provides synthetic yet realistic sensor data simulating equipment operation under normal and various failure conditions. Designed for predictive maintenance and machine learning, it includes sensor specifications, operational thresholds, and failure labels, allowing researchers to develop anomaly detection models without the constraints of sensitive real-world industrial data.
DataSense is a real-time Industrial IoT (IIoT) dataset from the Canadian Institute for Cybersecurity, combining synchronized sensor and network data from a 40-device testbed with over 15 types of industrial sensors for anomaly and intrusion detection research.
A merged and optimized dataset combining N-BaIoT (IoT-specific traffic) and UNSW-NB15 (general network threats) with feature engineering, dimensionality reduction, and benchmarked ML models (Decision Tree, SVM, Random Forest, Neural Network) for IoT anomaly detection, published in Scientific Reports.
Multimodal smart city dataset combining IoT sensor data (environmental, traffic, infrastructure) with ground-truth anomaly labels for urban safety applications, anomaly detection, and multi-source data fusion research in smart cities.
Complete and labeled IoT dataset from physical data center with NFC smart passive temperature sensors, containing normal operations and anomalous behaviors for time-series anomaly detection in critical infrastructure environments.
Real-time network traffic dataset from diverse IoT devices including normal behavior and various attacks (DDoS, brute-force, scans) for developing intrusion detection systems.