1+ from bottle import route , run , template ,post ,request
2+ import matplotlib .pyplot as plt
3+ import nltk
4+ import pandas as pd
5+ import scipy
6+ from nltk .util import ngrams
7+ from scipy .sparse import coo_matrix , hstack , vstack
8+ from sklearn .datasets import load_iris
9+ from sklearn .decomposition import TruncatedSVD
10+ from sklearn .feature_extraction .text import CountVectorizer , TfidfVectorizer
11+ from sklearn .linear_model import LogisticRegression
12+ from sklearn .metrics import f1_score
13+ from sklearn .model_selection import train_test_split
14+ import os
15+ import pickle
16+ import random
17+ import json
18+ import string
19+
20+ def get1Grams (payload_obj ):
21+ '''Divides a string into 1-grams
22+
23+ Example: input - payload: "<script>"
24+ output- ["<","s","c","r","i","p","t",">"]
25+ '''
26+ payload = str (payload_obj )
27+ ngrams = []
28+ for i in range (0 ,len (payload )- 1 ):
29+ ngrams .append (payload [i :i + 1 ])
30+ return ngrams
31+
32+ def get2Grams (payload_obj ):
33+ '''Divides a string into 2-grams
34+
35+ Example: input - payload: "<script>"
36+ output- ["<s","sc","cr","ri","ip","pt","t>"]
37+ '''
38+ payload = str (payload_obj )
39+ ngrams = []
40+ for i in range (0 ,len (payload )- 2 ):
41+ ngrams .append (payload [i :i + 2 ])
42+ return ngrams
43+
44+ def get3Grams (payload_obj ):
45+ '''Divides a string into 3-grams
46+
47+ Example: input - payload: "<script>"
48+ output- ["<sc","scr","cri","rip","ipt","pt>"]
49+ '''
50+ payload = str (payload_obj )
51+ ngrams = []
52+ for i in range (0 ,len (payload )- 3 ):
53+ ngrams .append (payload [i :i + 3 ])
54+ return ngrams
55+
56+
57+
58+ @post ('/hello/<name>' )
59+ def index (name ):
60+ data_params = request .json
61+ print (data_params )
62+ #postdata = name = request.forms.get("query_val")
63+ return_value = run_model (data_params )
64+ print ("return value " ,return_value )
65+ return str (return_value )
66+
67+ return "1"
68+ def run_model (param ):
69+ list_of_results = []
70+ check = 0
71+ for key in param :
72+ content = param [key ]
73+ q_val = content
74+ type_attack = None
75+ q_key = key
76+ df = pd .DataFrame ([[content ]],columns = ['content' ])
77+ df ['content' ] = df ['content' ].str .strip ('\n ' )
78+ df ['content' ] = df ['content' ].str .lower ()
79+ X1C = count_vectorizer_1grams .transform (df ["content" ])
80+ X2C = count_vectorizer_2grams .transform (df ["content" ])
81+ X3C = count_vectorizer_3grams .transform (df ["content" ])
82+ X1T = tfidf_vectorizer_1grams .transform (df ["content" ])
83+ X2T = tfidf_vectorizer_2grams .transform (df ["content" ])
84+ X3T = tfidf_vectorizer_3grams .transform (df ["content" ])
85+ X = hstack ([X1C ,X2C ,X3C ,X1T ,X2T ,X3T ])
86+ predicted = logistic_model .predict (X )
87+ predicted_sql = sql_model .predict_proba (X )[0 ][1 ]
88+ predicted_traverse = traverse_model .predict_proba (X )[0 ][1 ]
89+ predicted_xss = xss_model .predict_proba (X )[0 ][1 ]
90+ max_value = max (predicted_sql ,predicted_traverse ,predicted_xss )
91+ if max_value >= 0.5 :
92+ if max_value == predicted_sql :
93+ type_attack = "SQLi"
94+ elif max_value == predicted_traverse :
95+ type_attack = "Path Traversal"
96+ elif max_value == predicted_xss :
97+ type_attack = "XSS"
98+ dicc = {
99+ "type" :type_attack ,
100+ "param" : q_key ,
101+ "val" : q_val ,
102+ "confidence" : max_value
103+ }
104+ list_of_results .append (dicc )
105+ return json .dumps (list_of_results )
106+
107+ if __name__ == '__main__' :
108+ count_vectorizer_1grams = pickle .load (open ("c1g.vec" ,"rb" ))
109+ count_vectorizer_2grams = pickle .load (open ("c2g.vec" ,"rb" ))
110+ count_vectorizer_3grams = pickle .load (open ("c3g.vec" ,"rb" ))
111+ tfidf_vectorizer_1grams = pickle .load (open ("t1g.vec" ,"rb" ))
112+ tfidf_vectorizer_2grams = pickle .load (open ("t2g.vec" ,"rb" ))
113+ tfidf_vectorizer_3grams = pickle .load (open ("t3g.vec" ,"rb" ))
114+ logistic_model = pickle .load (open ("logistic.model" ,"rb" ))
115+ sql_model = pickle .load (open ("sql.model" ,"rb" ))
116+ traverse_model = pickle .load (open ("traverse.model" ,"rb" ))
117+ xss_model = pickle .load (open ("xss.model" ,"rb" ))
118+
119+ print ("valalal" )
120+ run (host = 'localhost' , port = 5000 ,reloader = True )
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