CIC-IoT2023: IoT Network Flow Features for Intrusion Detection

Official merged flow-feature CSV files for IoT intrusion detection from the CIC-IoT2023 project. The dataset is intended for building and evaluating machine learning models to identify malicious activity in IoT networks. The specific creator, collection time range, and dataset size are not detailed in the provided metadata.

Use Cases

• Train supervised machine learning models for IoT intrusion detection based on network flow features.
• Benchmark new anomaly detection algorithms against a standard IoT security dataset.
• Analyze patterns of malicious network traffic in IoT environments based on flow characteristics.

Strengths

• Dataset is described as the official release from the CIC-IoT2023 project, suggesting a standard benchmark.
• Data is provided as merged flow-feature CSV files, which likely facilitates direct analysis.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale model training.
• Last update date is unknown; freshness unverified.

Provenance

Source

CIC-IoT2023 project

Collection Method

Likely generated from simulated or captured IoT network traffic.