VDPython

VulDeePecker algorithm implemented in Python

VulDeePecker

Detects exploitable code in C/C++
Uses N-grams and deep learning with LSTMs to train detection model
Invents idea of code gadgets for semantically-related code
- Code gadgets are vectorized for input to neural network
- [Training/testing set for this project includes existing code gadgets and vulnerability classification]
Trained on two vulnerability types
Paper
GitHub

To run program, use this command: python vuldeepecker.py [gadget_file], where gadget_file is one of the text files containing a gadget set
Program has 3 parts:
- Performing gadget "cleaning"
  - Remove comments, string/character literals
  - Replacing all user-defined variables and functions with VAR# and FUN#, respectively
    - The # is an integer identifying the user-defined variable/function within the gadget
    - Note: this identifier only applies within the scope of the gadget
- Vectorize gadget
  - Gadgets are parsed, tokenized, and transformed to vectors of embeddings
  - Vectors are normalized to a constant length through either truncation or padding
- Train and test neural model
  - Gadget vectors are used as input to train the neural model
  - Data is split into training set and testing set
  - Neural model is trained, tested, and accuracy is reported

vuldeepecker.py
- Interface to project, uses functionality from other code files
- Fetches each gadget, cleans, buffers, trains Word2Vec model, vectorizes, passes to neural net
clean_gadget.py
- For each gadget, replaces all user variables with "VAR#" and user functions with "FUN#"
- Removes content from string and character literals
vectorize_gadget.py
- Converts gadgets into vectors
- Tokenizes gadget (converts to symbols, operators, keywords)
- Uses Word2Vec to convert tokens to embeddings
- Combines token embeddings in a gadget to create 2D gadget vector
blstm.py
- Defines Bidirectional Long Short Term Memory neural network for training/prediction of vulnerabilities
- Gets gadget vectors as input
- Implements functions for both training and testing the model
- Uses parameters defined in VulDeePecker paper