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--- |
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license: apache-2.0 |
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task_categories: |
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- feature-extraction |
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language: |
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- en |
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tags: |
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- certificates |
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- machine identity |
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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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configs: |
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- config_name: sample_data |
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data_files: Data/CertificateFeatures-sample.parquet |
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--- |
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# Machine Identity Spectra Dataset |
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<img src="https://huggingface.co/datasets/Venafi/Machine-Identity-Spectra/resolve/main/VExperimentalSpectra.svg" alt="Spectra Dataset" width="250"> |
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## Summary |
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Venafi is excited to release of the Machine Identity Spectra large 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 additional pre-processing for certain tasks. |
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This project is part of Venafi Athena. Venafi is committed to enabling the data science community to increase the adoption of machine learning techniques |
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to identify machine identity threats and solutions. |
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Phillip Maraveyias at Venafi is the lead researcher for this dataset. |
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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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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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conducted on the binary data features in the dataset. 10 clusters were generated and PCA conducted with the top 3 components preserved. |
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KMeans clustering was performed to generate a total of 10 clusters. In this case we are primarily |
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interested in visualizing the data and understanding better how it may be used, so the choice of 10 clusters is mostly |
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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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### Clusters in 3 Dimensions |
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## Contact |
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Please contact [email protected] if you have any questions about this dataset. |
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## References and Acknowledgement |
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The following papers provided inspiration for this project: |
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- Li, J.; Zhang, Z.; Guo, C. Machine Learning-Based Malicious X.509 Certificates’ Detection. Appl. Sci. 2021, 11, 2164. https://doi.org/ 10.3390/app11052164 |
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- Liu, J.; Luktarhan, N.; Chang, Y.; Yu, W. Malcertificate: Research and Implementation of a Malicious Certificate Detection Algorithm Based on GCN. Appl. Sci. 2022,12,4440. https://doi.org/ 10.3390/app12094440 |
