Datasets:
updates readme and adds sample data
Browse files- Data/CertificateFeatures-sample.parquet +3 -0
- README.md +7 -1
Data/CertificateFeatures-sample.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:8a98afde36b3a5187cfb7d032783f478f8157b2c2d9c59b869f29538d0f909e8
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size 90600349
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README.md
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@@ -11,6 +11,9 @@ tags:
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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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---
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# Venafi Machine Identity Spectra Dataset
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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
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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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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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size_categories:
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- 10M<n<100M
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pretty_name: Machine Identity Spectra Dataset
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configs:
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- config_name: sample_data
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data_files: Data/CertificateFeatures-data.parquet
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---
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# Venafi Machine Identity Spectra Dataset
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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 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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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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The Data folder also contains a 500k row sample of the data in parquet format. This is displayed in the Data Viewer
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for easy visual inspection of the dataset.
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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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