dtrizna/quovadis-ember

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:

@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 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