Normalized Datasets For Intrusion And Quantum Key Distribution Analysis

Five normalized datasets for intrusion detection systems (IDS), industrial control systems (ICS), and quantum key distribution (QKD) research, each including principal component analysis (PCA) variants. The dataset's creator, size, and last update date are unknown.

Use Cases

• Train classifiers to detect network intrusions using PCA-transformed features from the IDS datasets.
• Benchmark anomaly detection algorithms on ICS data with different PCA variant representations.
• Evaluate the security of QKD protocols using labeled data from the QKD dataset and its PCA components.

Strengths

• Contains five distinct datasets, enabling comparative analysis across domains.
• Each dataset includes PCA variants, providing dimensionality-reduced representations for modeling.

Limitations

• The exact number of rows, features, and class distributions for each dataset is unknown.
• The absence of column names and sample data prevents immediate understanding of feature semantics.

Provenance

Collection Method

Data appears to be collected and normalized from existing IDS, ICS, and QKD sources.