Webshell Detection

Webshell Detection Based on the Word Attention Mechanism

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Cloned from https://github.com/leett1/Programe/

Editing directory project.zip

Install packages:

gensim==3.8.1
python-Levenshtein==0.12.0
pathlib==1.0.1
numpy==1.19.2
tensorflow==1.14
keras==2.3.1
scikit-learn==0.24.1

Initializing a machine learning environment

Deploying a virtual environment Anaconda3

Install packages

yum install -y zlib-devel bzip2 bzip2-devel readline-devel sqlite sqlite-devel openssl-devel xz xz-devel libffi-devel
Install Anaconda3

curl -O https://repo.anaconda.com/archive/Anaconda3-2019.03-Linux-x86_64.sh

bash Anaconda3-2019.03-Linux-x86_64.sh
Edit file .bashrc

vim ~/.bashrc

Add this text to the end of the file:

export PATH="/home//anaconda3/bin:$PATH"

<user> - replace with user
source ~/.bash_profile
Install environment

conda create -n tf1_env

conda activate tf1_env

Project ML

cd /var
mkdir cnn_word2wec_sentence
chmod 775 cnn_word2wec_sentence
chown <user>:<group user> cnn_word2wec_sentence
cd /var/cnn_word2wec_sentence

Remote Python interpreter

Interpreter: /home/<user>/anaconda3/envs/tf1_env/bin/python

<user> - replace with user
Project migration: /var/cnn_word2wec_sentence

Python libraries

Project path

cd /var/cnn_word2wec_sentence
Activate env

conda activate tf1_env
Install TensorFlow

conda install tensorflow==1.14.0

conda install keras==2.3.1
Install requirements

pip install -r requirements.txt
Check

python -V

python -c 'import tensorflow as tf; print(tf.__version__)'

Train & test models

Model training: python3 one_attention_model.py

Output: one_attention_mode190626_dan.h5
Model training: python3 train_model.py

Output: two_attention_mode190317.h5
Model training: python3 word2vec_train.py

Output: word_train190313.model
Edit the file and run the model test: python3 test_1.py