README

README.md

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 license: apache-2.0
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+
+# 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}}
+```
+
+
+## Description
+
+This dataset contains EMBER features obtained from **93533** 32-bit portable executables (PE), used in *Quo Vadis* and *Nebula* papers. Features extraction scheme described in the original paper EMBER paper by Anderson and Roth: https://arxiv.org/abs/1804.04637.
+
+Complementary dataset with of emulated behavioral reports by Speakeasy is available at https://huggingface.co/datasets/dtrizna/quovadis-speakeasy.
+
+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 noted as `benign` are clean. All others represent malware distributed over 7 families. A specific number of files in each category is below. Notably, 
+
+Successfully processed files:
+```
+$ for file in $(find . | grep hashes); do wc -l $file; done
+24416 ./train/benign/hashes.txt
+4378 ./train/keylogger/hashes.txt
+8243 ./train/dropper/hashes.txt
+6548 ./train/coinminer/hashes.txt
+1697 ./train/rat/hashes.txt
+8733 ./train/trojan/hashes.txt
+9627 ./train/ransomware/hashes.txt
+11061 ./train/backdoor/hashes.txt
+
+7940 ./test/benign/hashes.txt
+1041 ./test/keylogger/hashes.txt
+252 ./test/dropper/hashes.txt
+1684 ./test/coinminer/hashes.txt
+1258 ./test/rat/hashes.txt
+1085 ./test/trojan/hashes.txt
+2139 ./test/ransomware/hashes.txt
+1940 ./test/backdoor/hashes.txt
+
+```
+
+Errors:
+```
+$ for file in $(find . | grep errors); do wc -l $file; done
+18 ./train/benign/errors.log
+0 ./train/keylogger/errors.log
+0 ./train/dropper/errors.log
+343 ./train/coinminer/errors.log
+0 ./train/rat/errors.log
+0 ./train/trojan/errors.log
+0 ./train/ransomware/errors.log
+1 ./train/backdoor/errors.log
+
+4 ./test/benign/errors.log
+0 ./test/keylogger/errors.log
+0 ./test/dropper/errors.log
+0 ./test/coinminer/errors.log
+0 ./test/rat/errors.log
+0 ./test/trojan/errors.log
+0 ./test/ransomware/errors.log
+0 ./test/backdoor/errors.log
+```