Realistic cybersecurity dataset with 14 attack types from 10+ IoT/IIoT device types including sensors, actuators, and industrial controllers. Supports centralized and federated learning with 61 optimized features.
Comprehensive dataset capturing cybersecurity threats and sustainability metrics in smart city IoT and edge networks, including communication behavior, energy consumption patterns, and attack scenarios.
500 simulation scenarios analyzing dynamic optimization techniques for energy efficiency in IoT sensor networks. Includes network lifetime, PDR, and energy consumption metrics.
1,000 records of simulated IoT network activity with blockchain-based security. Covers DDoS, malware, MITM attacks across device, network, and application layers.
1,191,264 network intrusion instances with 47 features. Large-scale dataset for training predictive models to detect IoT network attacks and anomalies.
IoT-based environmental perception data studying impact on university students' mental health. Integrates temperature, humidity, noise, and air quality sensors.
The most cited cybersecurity dataset worldwide with 2.8+ million network flows capturing 14 types of realistic attack scenarios including DDoS, brute force, botnet, and web attacks alongside benign traffic for advanced intrusion detection systems.
Comprehensive IoT network traffic dataset from an academic environment with 202,085 labeled records capturing benign and malicious activities for cybersecurity and anomaly detection research.
Real-time physiological and network-level data from a secure IoT healthcare monitoring system tracking 2000 patients, including biometric readings (heart rate, temperature, blood pressure) and network metadata for anomaly detection and cybersecurity analysis.
Real-time IoT sensor data from wearable health monitoring devices tracking patient vital signs including body temperature, blood pressure (systolic/diastolic), heart rate, and device battery levels for remote healthcare monitoring and predictive analytics.
Real-time IoT sensor data collected from industrial machines for predictive maintenance and anomaly detection in smart manufacturing environments, featuring temperature, vibration, pressure readings, and machine operational status for Industry 4.0 applications.