NSL-KDD: Network Intrusion Detection Dataset

Mireu-Lab provides a processed version of the NSL-KDD dataset, a benchmark for network intrusion detection systems. The data was converted from ARFF to CSV format and stored with float64 data types. The dataset was last updated on HuggingFace in July 2024.

Use Cases

• Train machine learning classifiers for attack detection based on network connection features.
• Benchmark anomaly detection algorithms using labeled network traffic data.
• Analyze patterns in network protocol and service usage for security research.

Strengths

• The dataset contains 151,165 non-null entries for core features like 'duration', 'protocol_type', and 'service'.
• Data has been processed for consistency, with numerical values stored as float64.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Mireu-Lab on HuggingFace, derived from the original NSL-KDD benchmark.

Collection Method

Converted from ARFF format to CSV and stored with float64 data types.

Freshness

Last updated 2024-07-18 13:29:50; freshness should be verified.