QuasarNix: Adversarially Robust Living-off-The-Land Reverse-Shell Detection Informed by Malicious Data Augmentation and Machine Learning

Description

This repository contains the datasets from the paper "Living-off-The-Land Reverse-Shell Detection by Informed Data Augmentation" by Dmitrijs Trizna, Luca Demetrio, Battista Biggio, and Fabio Roli. The paper pre-print is available on arXiv.

Security Information and Event Management (SIEM) cyber-threat detection solutions are highly extensible, with numerous public collections of signature-based rules. However, there are no known repositories with behavioral Machine Learning~(ML) cyber-threat detection heuristics. To address this gap, we develop framework for constructing ML detectors that leverages data augmentation.

Based on our framework, we are releasing production-ready adversarially robust ML detectors of Linux living-off-the-land (LOTL) reverse shells, trained on all known LOTL reverse shell manifestations identified by our threat intelligence under https://huggingface.co/dtrizna/QuasarNix and datasets here to foster adversarial ML research in cyber-security.

To the best of our knowledge, we are the first to publicly release generally applicable ML cyber-threat detection models suitable to wide variety of SIEM environments.

Code

https://github.com/dtrizna/QuasarNix

Cite Us

@misc{trizna2024livingoffthelandreverseshelldetectioninformed,
      title={Living-off-The-Land Reverse-Shell Detection by Informed Data Augmentation}, 
      author={Dmitrijs Trizna and Luca Demetrio and Battista Biggio and Fabio Roli},
      year={2024},
      eprint={2402.18329},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2402.18329}, 
}