# PerfSPEC Learning Phase  INTRO

Based in [PrefSPEC: Performance Profiling-based Proactive Security Policy Enforcement for Containers](https://ieeexplore.ieee.org/document/10577533) document presented in [1], thir repository contains source files used to generate and process data.

[PrefSPEC document](PerfSPEC.pdf)

[Presentación in Spanish](presentacion.pdf)

<div style="margin: auto">
 <a target="_blank" href="perfspec-learning/src/branch/main/presentacion.pdf"><img src="imgs/prefSPEC-learning.png" width="800"></a>
</div>

__PerfSPEC__ has three phases:

- Ranking
- Learning
- Runtime

This repository is focused in __Learning__ phase with attention on:

- Event logs info load and process
- Predictive learning model 

> Note: It is considered that __event data collection__ in `raw-audit-logs.log.gz` are realistic and representative to simulate
administrative operations.

## Files

- `raw-audit-logs.log` contains raw Kubernetes audit logs collected using the `audit-policy.yaml` audit policy.

Tools are distributed in directories:

- [Collect](collect)
- [Process](process)
- [Learning](learning)

As some tasks can be used in [Python](https://python.org) or [Rust](https://www.rust-lang.org/) there are directories for each programming languge inside directories tasks.  

Each `task/programming-language` may have a __data__ directory where processing output files is generated. 

### Collect data

If you wish to [collect](collect) your own dataset, there are several source files that might help:

- `collect/collect.py` is a script to trigger the installation and uninstallation of public Helm repositories.
- `collect/helm-charts.json` is a backup of Helm charts used at the time of the collection.

### Process data 


### Learning



## Reference

[1]: [H. Kermabon-Bobinnec et al., "PerfSPEC: Performance Profiling-based Proactive Security Policy Enforcement for Containers," in IEEE Transactions on Dependable and Secure Computing, doi: 10.1109/TDSC.2024.3420712.](https://ieeexplore.ieee.org/document/10577533)