Datasets:
Pmaraveyias
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updates datacard and visualisations
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ClusterAnalysis/clusters2d.png
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ClusterAnalysis/{clusters.png → clusters3d.png}
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README.md
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- security
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size_categories:
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- 10M<n<100M
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---
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# Venafi
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We are excited to announce the release of the Venafi Public Certificate Features dataset.
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This collection of data contains extracted features from 19m+ certificates discovered on the
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The features are a combination of X.509 certificate features, RFC5280 compliance checks,
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and other attributes intended to be used for clustering, features analysis, and a base for supervised learning tasks (labels not included).
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Some rows may contain nan values as well and as such could require some additional pre-processing for certain tasks.
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Venafi is excited to engage with the data science community to increase the adoption of machine learning techniques
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in the machine identity management and wider security domains.
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## Clustering and PCA Example
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To demonstrate a potential use of the data, clustering and Principal Component Analysis (PCA) were
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for illustrative purposes.
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The top three PCA components accounted for approximately 61%, 10%, and 6% of the total explained variance
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(for a total of 77% of the overall data variance).
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-
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- security
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size_categories:
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- 10M<n<100M
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pretty_name: Machine Identity Spectra Dataset
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# Venafi Machine Identity Spectra Dataset
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## Summary
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We are excited to announce the release of the Venafi Public Certificate Features dataset.
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This collection of data contains extracted features from 19m+ certificates discovered over HTTPS (port 443) on the
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public internet between July 20 and July 26, 2023.
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The features are a combination of X.509 certificate features, RFC5280 compliance checks,
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and other attributes intended to be used for clustering, features analysis, and a base for supervised learning tasks (labels not included).
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Some rows may contain nan values as well and as such could require some additional pre-processing for certain tasks.
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Venafi is excited to engage with the data science community to increase the adoption of machine learning techniques
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in the machine identity management and wider security domains.
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## Data Structure
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The extracted features are contained in the Data folder as certificateFeatures.csv.gz. The unarchived data size is
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approximately 10GB and contains 98 extracted features for approximately 19m certificates. A description of the features
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and expected data types is contained in the base folder as features.csv.
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## Clustering and PCA Example
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To demonstrate a potential use of the data, clustering and Principal Component Analysis (PCA) were
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for illustrative purposes.
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The top three PCA components accounted for approximately 61%, 10%, and 6% of the total explained variance
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(for a total of 77% of the overall data variance). Plots of the first 2 components in 2D space and top 3 components in
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3D space grouped into the 10 clusters are shown below.
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### Clusters in 2 Dimensions
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![](ClusterAnalysis/clusters2d.png)
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### Clusters in 3 Dimensions
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![](ClusterAnalysis/clusters3d.png)
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## Contact
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Please contact Phillip.Maraveyias@venafi.com and Ecosystem@Venafi.com if you have any questions about this dataset.
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