Recurrent neural networks for fuzz testing web browsers

This repository contains the code used for the paper "Recurrent neural networks for fuzz testing web browsers", which was accepted as a conference paper for the 21st annual International Conference on Information Security and Cryptology (ICISC) in Seoul, Korea.

Required Packages

• Numpy
• Tensorflow
• tqdm

pip install numpy tensorflow tqdm

Usage

Training

usage: stacked_rnn.py [-h] [--batch_size BATCH_SIZE] [--epochs EPOCHS]
                      [--learning_rate LEARNING_RATE] [--layers LAYERS]
                      [--internal_size INTERNAL_SIZE] [--seq_len SEQ_LEN]
                      [--training_set TRAINING_SET] [--load_size LOAD_SIZE]
                      [--out_folder OUT_FOLDER] [--split SPLIT]
                      [--cells {1,2}]

This script trains a stacked RNN model

optional arguments:
  -h, --help            show this help message and exit
  --batch_size BATCH_SIZE
                        size of the batch
  --epochs EPOCHS       number of training epochs
  --learning_rate LEARNING_RATE
                        learning rate
  --layers LAYERS       number of RNN layers
  --internal_size INTERNAL_SIZE
                        number of nodes inside a RNN cell
  --seq_len SEQ_LEN     sequence length used for training
  --training_set TRAINING_SET
                        path to the used training set
  --load_size LOAD_SIZE
                        defines how much data is loaded from the file in bytes
  --out_folder OUT_FOLDER
                        defines an output folder
  --split SPLIT         defines which split to use
  --cells {1,2}         sets the cell type to use: 1=LSTM, 2=GRU

This script trains the specified model. It creates tensorboard log events for validation loss, batch loss and accuracy. A checkpoint is generated for each epoch of training and each time a new lowest validation loss is achieved. In addition, a small sample is generated after each epoch of training.

Sampling

usage: sampling.py [-h] --chkpt_meta_file CHKPT_FN --cell {1,2} --cell_units
                   CELL_UNITS --layers LAYERS [--translation_dict TRANS_DICT]

required arguments:
  --chkpt_meta_file CHKPT_FN
                        the checkpoints meta file
  --cell {1,2}          cell type: 1=LSTM, 2=GRU
  --cell_units CELL_UNITS
                        number of hidden units in the RNN cells
  --layers LAYERS       number of layers
  --translation_dict TRANS_DICT
                        path to the saved translation dictionary
optional arguments:
  -h, --help            show this help message and exit

This script demonstrates how a trained model can be used to generate new HTML-tags. It uses the provided model to sample 20 lines from it and prints the results.