Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning
By Mohamed Amine Ferrag, Othmane Friha, Djallel Hamouda, Leandros A. Maglaras, Helge Janicke. IEEE Access, 2022
906 citations recorded by the source corpus.
In this paper, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning. Specifically, the dataset has been generated using a purpose-built IoT/IIoT testbed with a large representative set of devices, sensors, protocols and cloud/edge configurations. The IoT data are generated from various IoT devices (more than 10 types) such as Low-cost digital sensors for sensing temperature and humidity, Ultrasonic sensor, Water level detection sensor, pH Sensor Meter, Soil Moisture sensor, Heart Rate Sensor, Flame Sensor, etc.). Furthermore, we identify and analyze fourteen attacks related to IoT and IIoT connectivity protocols, which are categorized into five threats, including, DoS/DDoS attacks, Information gathering, Man in the middle attacks, Injection attacks, and Malware attacks. In addition, we extract features obtained from different sources, including alerts, system resources, logs, network traffic, and propose new 61 features with high correlations from 1176 found features. After processing and analyzing the proposed realistic cyber security dataset, we provide a primary exploratory data analysis and evaluate the performance of machine learning approaches (i.e., traditional machine learning as well as deep learning) in both centralized and federated learning modes. The Edge-IIoTset dataset can be publicly accessed from [1].
Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing
View Paper: https://www.semanticscholar.org/paper/d827388ec32e4da0fce0beefe21fd97da83d4485
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Citations in the corpus
- CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment (2023, methodology): “These features are extracted based on proposals present in the literature regarding IoT security [8,46].”
- Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities (2024, methodology): “In particular, the authors utilize a publically available dataset EdgeIIoTset [140] related to the Internet of Things (IoT) and Industrial IoT (IIoT) connectivity protocols, categorized into five types of threats: DoS/D-DoS attacks, information gathering, MITM attacks, injection attacks, and…”