Malware Classification: Models & Datasets

TL;DR

Yes, there are several pretrained malware classification models and datasets available. This guide covers where to find them, how to use some popular options, and things to consider when choosing one.

1. Public Malware Datasets

A good model needs data! Here’s a breakdown of useful datasets:

• VirusShare: A large collection of real-world malware samples. Requires registration and agreement to their terms. https://virusshare.com/
• MalwareBazaar: Maintained by abuse.ch, this provides a wealth of malware data with tags and analysis reports. https://mbazaar.abuse.ch/
• VX-Underground: A massive archive of malware source code, binaries, and documentation. https://vx-underground.org/ (often requires careful handling due to the nature of the content).
• EML Dataset: Focuses on malicious email samples with attachments. Useful for training models that detect malware delivered via email. https://www.kaggle.com/datasets/rtatman/malware-email-dataset
• CICIDS2019: A comprehensive intrusion detection dataset, including benign and malicious network traffic. https://www.cicdatasets.com/ids-2019/

2. Pretrained Malware Classification Models

These models have already been trained on malware data and can be used directly or fine-tuned for your specific needs.

• YARA Rules: Not a model in the traditional sense, but YARA is a pattern matching tool widely used to identify malware families. Many pre-written rules are available online. https://github.com/VirusTotal/yara
• Machine Learning Models (Scikit-learn): You can find examples of models trained with Scikit-learn on datasets like CICIDS2019, often using features extracted from network traffic or file headers. Search GitHub for “malware classification scikit-learn”.
• Deep Learning Models (TensorFlow/PyTorch): More complex models are available, typically based on Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs). Look for projects using libraries like TensorFlow or PyTorch. https://github.com/tensorflow/tensorflow and https://pytorch.org/
• Hybrid Analysis: Offers API access to their malware analysis platform, including classification results based on their own models. (Commercial service). https://www.hybrid-analysis.com/

3. Using a Pretrained Model – Example with Scikit-learn

This is a simplified example to illustrate the process. You’ll need Python and the Scikit-learn library installed.

1. Install Scikit-learn:

   pip install scikit-learn

2. Load a pre-trained model (this assumes you have a saved model file, e.g., ‘malware_model.pkl’):

   import joblib
   model = joblib.load('malware_model.pkl')

3. Prepare your data: You’ll need to extract the same features from your files as were used to train the model.

   For example, if the model was trained on file size and entropy:

   import os
   import math
   
   def calculate_entropy(file_path):
     with open(file_path, 'rb') as f:
       data = f.read()
       if not data:
         return 0
       histogram = {}
       for byte in data:
         if byte in histogram:
           histogram[byte] += 1
         else:
           histogram[byte] = 1
       total_bytes = len(data)
       entropy = 0
       for count in histogram.values():
         probability = float(count) / total_bytes
         entropy -= probability * math.log2(probability)
       return entropy
   
   def extract_features(file_path):
     try:
       file_size = os.path.getsize(file_path)
       entropy = calculate_entropy(file_path)
       return [file_size, entropy]
     except Exception as e:
       print(f"Error processing {file_path}: {e}")
       return None

4. Make a prediction:

   file_to_predict = 'suspicious_file.exe'
   features = extract_features(file_to_predict)
   if features:
     prediction = model.predict([features])[0]
     print(f"Prediction for {file_to_predict}: {prediction}")

4. Important Considerations

• Data Quality: The accuracy of a model depends heavily on the quality and representativeness of the training data.
• Feature Engineering: Selecting the right features is crucial for good performance.
• Model Updates: Malware evolves constantly, so models need to be regularly updated with new samples.
• False Positives/Negatives: No model is perfect. Consider the trade-off between false positives (incorrectly identifying benign files as malicious) and false negatives (missing actual malware).
• Evasion Techniques: Malware authors use techniques to evade detection, so models need to be robust against these attacks.