Requirements

python 3.5

Keras 1.12.0

Tensorflow 1.1.0

sklearn

numpy

pandas

matplotlib

Usage:

unzip ./dataset/imgfeature_extent_10000.zip to get the whole dataset_X

python3 model.py will train the model and save it as Bpnn_features10000_model_batchSize512

python3 example.py is to predict single example

Dataset

from Kaggle ：

https://www.kaggle.com/c/malware-classification/data

unzip the ./dataset/imgfeature_extent_10000.zip to get the whole dataset_X

trainLables2.csv is the whole dataset_Y

The dataset contain 2w example which is divided as nine classes.

Besides,It is not balance.

I choose 0.15 dataset as test data

each example contains 2w featutes. one feature actually is a pixel value from 0 to 0xff.

Here is one grey picture.

Model

Simple Three Dense Model, which detail is shown below.

Train

Comparing different epochs and batch size how to change the training result.

So we choose

Batch Size　＝ 　512

Epochs = 30

Dropout = 0.1

Then we get the training acc&loss curve.

Test

Confuse matrix

[193 0 9 0 0 1 0 0 0]

[ 1 360 0 0 0 0 0 0 0]

[ 1 0 389 0 0 0 0 0 0]

[ 4 0 0 56 0 0 0 0 0 ]

[ 1 0 1 0 2 4 0 0 0 ]

[ 6 0 2 0 0 105 0 0 0]

[ 2 0 18 0 0 0 37 0 0]

[ 0 0 0 0 0 0 0 194 3]

[ 0 0 0 0 0 0 0 5 140]

change the value into pixel to get below picture.