# kairos

**Repository Path**: chen_minghao_2014/kairos

## Basic Information

- **Project Name**: kairos
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-04-27
- **Last Updated**: 2024-04-27

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Kairos

This repository contains the implementation of the approach proposed 
in the paper 
"_KAIROS: Practical Intrusion Detection and Investigation using Whole-system Provenance_".

Please cite this paper if you use the model or any code
from this repository in your own work:
```
@inproceedings{cheng2024kairos,
  title={KAIROS: Practical Intrusion Detection and Investigation using Whole-system Provenance},
  author={Cheng, Zijun and Lv, Qiujian and Liang, Jinyuan and Wang, Yang and Sun, Degang and Pasquier, Thomas and Han, Xueyuan},
  booktitle={2024 IEEE Symposium on Security and Privacy (SP)},
  year={2024},
  organization={IEEE}
}
```

We provide a [demo](DARPA/README.md)
to illustrate step-by-step
how you can run the code end-to-end.
Additionally, we provide IPython notebook
scripts for all of our experiments.
> Due to the extended amount of time it takes to
> train a model, we also provide pre-trained models
> of our experimental datasets.
> You can download these models directly from our [Google Drive](https://drive.google.com/drive/u/0/folders/1YAKoO3G32xlYrCs4BuATt1h_hBvvEB6C).

Our paper and [the supplementary material](supplementary-material.pdf)
contain links to all publicly available datasets used in our experiments.