This repository is part of the following publication: https://dl.acm.org/doi/10.1145/3560830.3563726

Quo Vadis: Hybrid Machine Learning Meta-Model Based on Contextual and Behavioral Malware Representations

⚠️ The model is a research prototype, provided as-is, without warranty of any kind, in a pre-alpha state.

Dataset structure used for model pre-training is as follows:

Raw PE samles and in-the-wild filepaths are not disclosed due to Privacy Policy. However,

• PE emulation dataset available in emulation.dataset
• Filepath dataset (open sources only, in-the-wild paths used for pre-training are excluded):
  • augmented samples and logic
  • paths from clean Windows 10 host

If you are inspired by the work or use data, please cite us:

@inproceedings{10.1145/3560830.3563726,
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}
}

Hybrid, modular structure for malware classification. Supported modules:

• 1D convolution neural network analysis of API call sequence obtained from Speakeasy emulator
• 1D convolution neural network analysis of filepath at the moment of execution (Kyadige and Rudd et al., https://arxiv.org/abs/1905.06987)
• 'Ember' Gradient Boosted Decision Tree (GBDT) model (Anderson and Roth, https://arxiv.org/abs/1804.04637)
• 'MalConv' byte-level convolutional neural network (Raff et al., https://arxiv.org/abs/1710.09435)

Tested on Python 3.8.x - 3.9.x. Because of a large number of dependencies with specific versions (due to pre-trained machine learning models), we suggest using a virtual environment or conda:

% python3 -m venv QuoVadisEnv
% source QuoVadisEnv/bin/activate
(QuoVadisEnv)% python -m pip install -r requirements.txt

API interface is available under models.py.

from models import CompositeClassifier

classifier = CompositeClassifier(meta_model = "MultiLayerPerceptron", 
                                   modules = ["ember", "emulation"],
                                   root = "/home/user/quo.vadis/",
                                   load_meta_model = True)

Available pretrained configurations:

meta_model = 'LogisticRegression', modules = ['ember', 'emulation', 'filepaths', 'malconv']
meta_model = 'MultiLayerPerceptron', modules = ['ember', 'emulation']
meta_model = 'MultiLayerPerceptron', modules = ['ember', 'emulation', 'filepaths']
meta_model = 'MultiLayerPerceptron', modules = ['ember', 'emulation', 'filepaths', 'malconv']
meta_model = 'MultiLayerPerceptron', modules = ['emulation']
meta_model = 'MultiLayerPerceptron', modules = ['filepaths']
meta_model = 'XGBClassifier', modules = ['ember', 'emulation']
meta_model = 'XGBClassifier', modules = ['ember', 'emulation', 'filepaths']
meta_model = 'XGBClassifier', modules = ['ember', 'emulation', 'filepaths', 'malconv']
meta_model = 'XGBClassifier', modules = ['emulation']
meta_model = 'XGBClassifier', modules = ['filepaths']

pefiles = os.listdir("/path/to/PE/samples")
x = classifier.preprocess_pelist(pefiles)
probs = classifier.predict_proba(x)

You can use predict_proba_pelist() instead of predict_proba() to get probabilities out of the PE list right away instead of a preprocessed array:

probs = classifier.predict_proba_pelist(pefiles)

Given that filepaths is specified in modules = , you have to specify the filepaths of the PE sample at the moment of execution using the pathlist= argument:

filepaths = pd.read_csv(filepaths.csv, header=None)
probs = classifier.predict_proba_pelist(pefiles, pathlist=filepaths.values.tolist())

Note! len(pefiles) == len(filepaths)

Using the fit_pelist() method and providing ground true labels for PE files -- malware (1) or benign (0):

labels = load_labels()
classifier.fit_pelist(pefiles, labels, pathlist=filepaths.values.tolist())

An example usage can be found under example.py:

# python example.py --example --how ember emulation filepaths

[*] Loading model...
WARNING:root:[!] Loading pretrained weights for ember model from: ./modules/sota/ember/parameters/ember_model.txt
WARNING:root:[!] Loading pretrained weights for filepath model from: ./modules/filepath/pretrained/torch.model
WARNING:root:[!] Using speakeasy emulator config from: ./data/emulation.dataset/sample_emulation/speakeasy_config.json
WARNING:root:[!] Loading pretrained weights for emulation model from: ./modules/emulation/pretrained/torch.model
WARNING:root:[!] Loading pretrained weights for late fusion MultiLayerPerceptron model from: ./modules/late_fustion_model/MultiLayerPerceptron15_ember_emulation_filepaths.model

[*] Legitimate 'calc.exe' analysis...
WARNING:root:[!] Taking current filepath for: evaluation/adversarial/samples_goodware/calc.exe
WARNING:root: [+] 0/0 Finished emulation evaluation/adversarial/samples_goodware/calc.exe, took: 0.19s, API calls acquired: 6
[!] Given path evaluation/adversarial/samples_goodware/calc.exe, probability (malware): 0.000005
[!] Individual module scores:

       ember  filepaths  emulation
0  0.000015    0.00319   0.062108 

WARNING:root: [+] 0/0 Finished emulation evaluation/adversarial/samples_goodware/calc.exe, took: 0.11s, API calls acquired: 6
[!] Given path C:\users\myuser\AppData\Local\Temp\exploit.exe, probability (malware): 0.549334
[!] Individual module scores:

       ember  filepaths  emulation
0  0.000015   0.999984   0.062108 

[*] BoratRAT analysis...
WARNING:root: [+] 0/0 Finished emulation ./b47c77d237243747a51dd02d836444ba067cf6cc4b8b3344e5cf791f5f41d20e, took: 0.25s, API calls acquired: 194

[!] Given path %USERPROFILE%\Downloads\BoratRat.exe, probability (malware): 0.9997
[!] Individual module scores:

       ember  filepaths  emulation
0  0.035511   0.999602    0.96526 

WARNING:root: [+] 0/0 Finished emulation ./b47c77d237243747a51dd02d836444ba067cf6cc4b8b3344e5cf791f5f41d20e, took: 0.25s, API calls acquired: 194

[!] Given path C:\windows\system32\calc.exe, probability (malware): 0.0392
[!] Individual module scores:

       ember  filepaths  emulation
0  0.035511   0.086567    0.96526 

More detailed information about modules and individual tests:

• ./modules/emulation/
• ./modules/filepaths/
• ./modules/sota/

Note! Parameters for the sota models can be downloaded from here.

Performance of this model on the proprietary dataset: ~90k PE samples with filepaths from real-world systems:

DET and ROC curves:

Detection rate with fixed False Positive rate:

• Experiments with retrained MalConv / Ember weights -- it makes sense to evaluate them on the same distribution
  • Note: this, however, does not matter since our goal is not to compare our modules with MalConv / Ember directly but to improve them. For this reason, it is even better to have original parameters. The main takeaway -- adding multiple modules together allows boosting results drastically. At the same time, each is noticeably weaker (even the API call module, which is trained on the same distribution).
• Run GAMMA against composite solution (not just ember/malconv modules) - it looks like attacks are highly targeted. Interesting if it will be able to generate evasive samples against a complete pipeline .. (however, defining that in secml_malware might be painful ...)
• Work on CompositeClassifier() API interface:
  • make it easy to take a PE sample(s) & additional document options (providing PE directory, predefined emulation report directory, etc.)
  • .update() to overtrain network with own examples that were previously flagged incorrectly
  • work without submitted filepath (only PE mode) - provide paths as separate argument to .fit()?
• Additional modules:
  • (a) Autoruns checks (see Sysinternals book for a full list of registries analyzed)
  • (b) network connection information
  • etc.