MalConv-keras

A Keras implementation of MalConv and adversarial sample

Desciprtion

This is the implementation of MalConv proposed in Malware Detection by Eating a Whole EXE which can be used for any very long sequence classification.

The adversarial samples are crafted by padding some bytes to the input file. It would fail if the origin file length exceeds the model's input size.

Enjoy !

Requirement

python3 (3.5.2)
numpy (1.13.1)
pandas (0.22.0)
pickle (0.7.4)
keras (2.1.5)
tensorflow (1.6.0)
sklearn

Get started

Clone the repository

git clone https://github.com/j40903272/MalConv-keras

Prepare data

Prepare a csv file with filenames(absolute or relative path) and labels in the <filename, label> format

0778a070b283d5f4057aeb3b42d58b82ed20e4eb_f205bd9628ff8dd7d99771f13422a665a70bb916, 0
fbd1a4b23eff620c1a36f7c9d48590d2fccda4c2_cc82281bc576f716d9a0271d206beb81ad078b53, 0

see more in example.csv (1:benign, 0:malicious)

Training

python3 train.py example.csv
python3 train.py example.csv --resume

Predict

python3 predict.py example.csv
python3 predict.py example.csv --result_path saved/result.csv

Preprocess

If you require the preprocessed data, run the following

python3 preprocess.py example.csv
python3 preprocess.py example.csv --save_path saved/preprocess_data.pkl

Adversarial

Try different --step_size, it's quite sensitive

python3 gen_adversarial.py example.csv
python3 gen_adversarial.py example.csv --save_path saved/adversarial_samples --pad_percent 0.1

### for multiple class classification
python3 gen_adversarial2.py example.csv --class 1

The process log format would be <filename, original score, file length, pad length, loss, predict score> as in adversarial_log.csv

< Notice > The generated padding bytes sometimes cannot be corrected encoded, a workaround is as follow :

# Read bytes then tokenize
byte_content = open('target', 'rb').read()
content = [chr(i) for i in byte_content]

Parameters

Find out more options with -h

python3 train.py -h

  -h, --help
  --batch_size BATCH_SIZE
  --verbose VERBOSE
  --epochs EPOCHS
  --limit LIMIT
  --max_len MAX_LEN
  --win_size WIN_SIZE
  --val_size VAL_SIZE
  --save_path SAVE_PATH
  --save_best
  --resume
  
python3 predict.py -h
python3 preprocess.py -h

Logs and checkpoint

The default path for output files would all be in saved/

Example

from malconv import Malconv
from preprocess import preprocess
import utils

model = Malconv()
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc'])

df = pd.read_csv(input.csv, header=None)
filenames, label = df[0].values, df[1].values
data = preprocess(filenames)
x_train, x_test, y_train, y_test = utils.train_test_split(data, label)

history = model.fit(x_train, y_train)
pred = model.predict(x_test)