Edge IIoT Network Traffic Dataset For Cybersecurity Analysis

Edge-IIoTset is a cybersecurity dataset capturing network traffic from an Industrial Internet of Things testbed. The dataset is designed for machine learning research on attack detection and network behavior analysis. It was created by researchers and published on the OpenML platform.

Use Cases

• Classify network traffic as benign or malicious using features like packet size, protocol type, and flow duration.
• Detect specific attack types such as DDoS or malware by analyzing patterns in source/destination ports and TCP flags.
• Perform feature importance analysis to identify the most predictive network attributes for cyber threat identification.
• Benchmark machine learning models like Random Forest or Neural Networks on their ability to predict attack categories from traffic logs.

Strengths

• Dataset is specifically cleaned and prepared for machine learning tasks, reducing initial preprocessing effort.
• Focuses on the emerging and critical domain of Industrial IoT (IIoT) security, addressing a relevant research gap.

Limitations

• The specific number of rows, columns, and data completeness are unknown, making it difficult to assess scale and suitability.
• Without column details, the exact feature engineering and label granularity cannot be verified.

Provenance

Source

OpenML

Collection Method

Collected from a simulated or real Industrial IoT network testbed environment.

Time Range

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Freshness

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Geography

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