| --- |
| license: apache-2.0 |
| task_categories: |
| - feature-extraction |
| language: |
| - en |
| tags: |
| - certificates |
| - machine identity |
| - security |
| size_categories: |
| - 10M<n<100M |
| pretty_name: Machine Identity Spectra Dataset |
| configs: |
| - config_name: sample_data |
| data_files: Data/CertificateFeatures-sample.parquet |
| --- |
| |
| # Machine Identity Spectra Dataset |
| <img src="https://huggingface.co/datasets/Venafi/Machine-Identity-Spectra/resolve/main/VExperimentalSpectra.svg" alt="Spectra Dataset" width="250"> |
|
|
| ## Summary |
| Venafi is excited to release of the Machine Identity Spectra large dataset. |
| This collection of data contains extracted features from 19m+ certificates discovered over HTTPS (port 443) on the |
| public internet between July 20 and July 26, 2023. |
| The features are a combination of X.509 certificate features, RFC5280 compliance checks, |
| and other attributes intended to be used for clustering, features analysis, and a base for supervised learning tasks (labels not included). |
| Some rows may contain nan values as well and as such could require additional pre-processing for certain tasks. |
|
|
| This project is part of Venafi Athena. Venafi is committed to enabling the data science community to increase the adoption of machine learning techniques |
| to identify machine identity threats and solutions. |
| Phillip Maraveyias at Venafi is the lead researcher for this dataset. |
|
|
| ## Data Structure |
| The extracted features are contained in the Data folder as certificateFeatures.csv.gz. The unarchived data size is |
| approximately 10GB and contains 98 extracted features for approximately 19m certificates. A description of the features |
| and expected data types is contained in the base folder as features.csv. |
|
|
| The Data folder also contains a 500k row sample of the data in parquet format. This is displayed in the Data Viewer |
| for easy visual inspection of the dataset. |
|
|
| ## Clustering and PCA Example |
|
|
| To demonstrate a potential use of the data, clustering and Principal Component Analysis (PCA) were |
| conducted on the binary data features in the dataset. 10 clusters were generated and PCA conducted with the top 3 components preserved. |
|
|
| KMeans clustering was performed to generate a total of 10 clusters. In this case we are primarily |
| interested in visualizing the data and understanding better how it may be used, so the choice of 10 clusters is mostly |
| for illustrative purposes. |
|
|
| The top three PCA components accounted for approximately 61%, 10%, and 6% of the total explained variance |
| (for a total of 77% of the overall data variance). Plots of the first 2 components in 2D space and top 3 components in |
| 3D space grouped into the 10 clusters are shown below. |
|
|
| ### Clusters in 2 Dimensions |
|  |
|
|
| ### Clusters in 3 Dimensions |
|  |
|
|
| ## Contact |
| Please contact athena-community@venafi.com if you have any questions about this dataset. |
|
|
| ## References and Acknowledgement |
| The following papers provided inspiration for this project: |
| - 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 |
| - 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 |
