Update README.md

README.md

@@ -1,35 +1,36 @@
----
-license: apache-2.0
----
-
-# QuasarNix: Adversarially Robust Living-off-The-Land Reverse-Shell Detection Informed by Malicious Data Augmentation and Machine Learning
-
-<img src="https://raw.githubusercontent.com/dtrizna/QuasarNix/main/img/quasaroutflow.png" width=600>
-
-## 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](https://arxiv.org/abs/2402.18329).
-
-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
-
-```bibtex
-@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}, 
-}
-```
+---
+license: apache-2.0
+pretty_name: quasarnix
+---
+
+# QuasarNix: Adversarially Robust Living-off-The-Land Reverse-Shell Detection Informed by Malicious Data Augmentation and Machine Learning
+
+<img src="https://raw.githubusercontent.com/dtrizna/QuasarNix/main/img/quasaroutflow.png" width=600>
+
+## 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](https://arxiv.org/abs/2402.18329).
+
+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
+
+```bibtex
+@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}, 
+}
+```