QGAN-NAD: 5 Normalized Network Traffic Datasets for Quantum ML

Five normalized network traffic datasets prepared for quantum machine learning baseline experiments. The datasets are hosted on Kaggle, but specific details about their origin, size, and creation date are not provided in the available metadata.

Use Cases

• Establishing performance baselines for quantum anomaly detection models based on the normalized network traffic data.
• Comparing classical and quantum machine learning algorithms using the provided preprocessed datasets.
• Developing and testing new quantum algorithms for cybersecurity applications based on network traffic patterns.

Strengths

• Contains five distinct datasets, providing multiple test scenarios.
• Data is pre-normalized, which may reduce preprocessing effort for initial experiments.

Limitations

• Row count, column definitions, and file formats are unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Kaggle

Collection Method

Likely gathered from network traffic sources and normalized for ML readiness.

Time Range

null

Freshness

Last update date is unknown; freshness unverified.

Geography

null