|
--- |
|
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-data.parquet |
|
--- |
|
|
|
# Venafi Machine Identity Spectra Dataset |
|
|
|
## Summary |
|
|
|
We are excited to announce the release of the Venafi Public Certificate Features 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. |
|
|
|
Venafi is excited to engage with the data science community to increase the adoption of machine learning techniques |
|
in the machine identity management and wider security domains. |
|
|
|
## 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 phillip.maraveyias@venafi.com and ecosystem@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 |
