Speakeasy emulation reports

.gitattributes

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README.md

@@ -1,3 +1,83 @@
 ---
 license: apache-2.0
 ---
+
+# About Dataset
+
+## Citation
+
+This dataset was created and further refined as part of the following two publications:
+
+- "Quo Vadis: Hybrid Machine Learning Meta-Model Based on Contextual and Behavioral Malware Representations", Trizna et al., 2022, https://dl.acm.org/doi/10.1145/3560830.3563726
+
+- "Nebula: Self-Attention for Dynamic Malware Analysis", Trizna et al., 2024, https://ieeexplore.ieee.org/document/10551436
+
+If you used it in your research, please cite us:
+
+```bibtex
+@inproceedings{quovadis,
+author = {Trizna, Dmitrijs},
+title = {Quo Vadis: Hybrid Machine Learning Meta-Model Based on Contextual and Behavioral Malware Representations},
+year = {2022},
+isbn = {9781450398800},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3560830.3563726},
+doi = {10.1145/3560830.3563726},
+booktitle = {Proceedings of the 15th ACM Workshop on Artificial Intelligence and Security},
+pages = {127–136},
+numpages = {10},
+keywords = {reverse engineering, neural networks, malware, emulation, convolutions},
+location = {Los Angeles, CA, USA},
+series = {AISec'22}
+}
+@ARTICLE{nebula,
+  author={Trizna, Dmitrijs and Demetrio, Luca and Biggio, Battista and Roli, Fabio},
+  journal={IEEE Transactions on Information Forensics and Security}, 
+  title={Nebula: Self-Attention for Dynamic Malware Analysis}, 
+  year={2024},
+  volume={19},
+  number={},
+  pages={6155-6167},
+  keywords={Malware;Feature extraction;Data models;Analytical models;Long short term memory;Task analysis;Encoding;Malware;transformers;dynamic analysis;convolutional neural networks},
+  doi={10.1109/TIFS.2024.3409083}}
+```
+
+Arxiv references of both papers: arxiv.org/abs/2310.10664 and arxiv.org/abs/2208.12248.
+
+## Description
+
+This dataset contains behavioral reports obtained with Speakeasy emulator from **93533** 32-bit portable executables (PE).
+
+This is complementary dataset to https://huggingface.co/datasets/dtrizna/quovadis-ember, which represents static EMBER features of the same malware samples.
+
+To reflect concept drift in malware:
+
+- 76126 files that form a training set were collected in Jan 2022.
+- 17407 files that form a test set were collected in Apr 2022.
+
+## Labels
+
+Files located in `report_clean` and `report_windows_syswow64` are clean (benign). All others represent malware distributed over 7 families. A specific number of files in each folder:
+
+- Training set
+    - report_backdoor : 11062
+    - report_clean : 24434
+    - report_coinminer : 6891
+    - report_dropper : 8243
+    - report_keylogger : 4378
+    - report_ransomware : 9627
+    - report_rat : 1697
+    - report_trojan : 8733
+    - report_windows_syswow64 : 236
+
+- Test set
+    - report_backdoor : 1940
+    - report_clean : 7944
+    - report_coinminer : 1684
+    - report_dropper : 252
+    - report_keylogger : 1041
+    - report_ransomware : 2139
+    - report_rat : 1258
+    - report_trojan : 1085
+    - report_windows_syswow64 : 59

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