mangya_waf.py

# -*- coding: utf-8 -*-
"""mms waf modeling ml algo.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1h2OcDhgHiLVx6_u5HlfTz9roYKkPH9-K

# Modeling and evaluation of machine learning model

Import all dependencies
"""

# Commented out IPython magic to ensure Python compatibility.
# %matplotlib inline
def warn(*args, **kwargs):
    pass
import warnings
warnings.warn = warn

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import string
import pickle
from sklearn.externals import joblib
from IPython.display import display

import sys
"""
import warnings
warnings.filterwarnings("ignore")
with warnings.catch_warnings():
    warnings.simplefilter("ignore")
warnings.simplefilter(action='ignore', category=FutureWarning)

import sys

if not sys.warnoptions:
    import warnings
    warnings.simplefilter("ignore")
"""   
#evaluations
payload = str(sys.argv[1])
data = pd.read_csv("/home/ubuntu/tools/mms_waf/final-allpayload.csv",index_col="index")
#data

"""Selecting dependent and independent variables"""

Y = data['is_malicious']
"""independent_variables = data.columns
independent_variables = independent_variables.delete(1)
independent_variables"""
X = data.iloc[:,3:]


"""# Modeling data with Xgboost Classifier"""

import xgboost as xgb
#?xgb.XGBClassifier()
#xgb_classifer = xgb.XGBClassifier()

xgb_classifer_joblib = joblib.load("/home/ubuntu/tools/mms_waf/mms_waf_joblib")
"""
with open("/home/ubuntu/tools/mms_waf/mms_maf_final","rb") as file:
  xgb_classifer_pickle = pickle.load(file)

xgb_classifer_pickle.fit(X,Y)
"""

"""
# Commented out IPython magic to ensure Python compatibility.
# %time xgb_classifer.fit(X,Y)

data['predicted_is_malicious'] = xgb_classifer_pickle.predict(X)

data.head(30)

data[["is_malicious","predicted_is_malicious"]]

#?plt.plot()
"""

"""# Integration with website"""

independent_variables=['length', 'non-printable','punctuation', 'min-byte', 'max-byte', 'mean-byte', 'std-byte','distinct-byte', 'sql-keywords', 'js-keywords']
independent_variables

independent_variables=data.columns

sql_keywords = pd.read_csv('/home/ubuntu/tools/mms_waf/SQLKeywords.txt', index_col=False)
js_keywords = pd.read_csv("/home/ubuntu/tools/mms_waf/JavascriptKeywords.txt",index_col=False)

def calculate_features_and_predict(payload):
  features = {} 
  payload = str(payload)
  features['length'] = len(payload)
  features['non-printable'] = len([1 for letter in payload if letter not in string.printable])
  features['punctuation'] = len([1 for letter in payload if letter in string.punctuation])
  features['min-byte'] = min(bytearray(payload,'utf-8'))
  features['max-byte'] = max(bytearray(payload,'utf-8'))
  features['mean-byte'] = np.mean(bytearray(payload,'utf-8'))
  features['std-byte'] = np.std(bytearray(payload,'utf-8'))
  features['distinct-byte'] = len(set(bytearray(payload,'utf-8')))
  features['sql-keywords'] = len([1 for keyword in sql_keywords['Keyword'] if str(keyword).lower() in payload.lower()])
  features['js-keywords'] = len([1 for keyword in js_keywords['Keyword'] if str(keyword).lower() in payload.lower()])
  #payload_df = pd.DataFrame(data=features,index=[0],columns=independent_variables)
  payload_df = pd.DataFrame(features,index=[0])
  #display(payload_df)
  result = xgb_classifer_joblib.predict(payload_df)
  #display(result)
  r = result[0]
  #print(r)
  if(r > 0):
    print("malicious - 403 error")
    
  else:
    print("safe")

"""
calculate_features_and_predict("<>")

payload=''
while (payload != 'exit' ):
  payload = input("Enter payload")
  result = calculate_features_and_predict(payload)
  if(result > 0):
    print(f"Your payload {payload} is malicious - 403 error\n")
  else:
    print(f"Your payload {payload} is safe 200 OK\n")
"""


calculate_features_and_predict(payload)