import re
import numpy as np
from sklearn import cross_validation
from sklearn import datasets
from sklearn import svm
from sklearn.externals import joblib
from sklearn.metrics import classification_report
from sklearn import metrics



x = []
y = []


def get_len(url):
 return len(url)

def get_url_count(url):
 if re.search('(http://)|(https://)', url, re.IGNORECASE) :
 return 1
 else:
 return 0

def get_evil_char(url):
 return len(re.findall("[<>,\'\"/]", url, re.IGNORECASE))

def get_evil_word(url):
 return len(re.findall("(alert)|(script=)(%3c)|(%3e)|(%20)|(onerror)|(onload)|(eval)|(src=)|(prompt)",url,re.IGNORECASE))

def get_last_char(url):
 if re.search('/$', url, re.IGNORECASE) :
 return 1
 else:
 return 0

def get_feature(url):
 return [get_len(url),get_url_count(url),get_evil_char(url),get_evil_word(url),get_last_char(url)]

def do_metrics(y_test,y_pred):
 print "metrics.accuracy_score:"
 print metrics.accuracy_score(y_test, y_pred)
 print "metrics.confusion_matrix:"
 print metrics.confusion_matrix(y_test, y_pred)
 print "metrics.precision_score:"
 print metrics.precision_score(y_test, y_pred)
 print "metrics.recall_score:"
 print metrics.recall_score(y_test, y_pred)
 print "metrics.f1_score:"
 print metrics.f1_score(y_test,y_pred)

def etl(filename,data,isxss):
 with open(filename) as f:
 for line in f:
 f1=get_len(line)
 f2=get_url_count(line)
 f3=get_evil_char(line)
 f4=get_evil_word(line)
 data.append([f1,f2,f3,f4])
 if isxss:
 y.append(1)
 else:
 y.append(0)
 return data

etl('../data/xss-200000.txt',x,1)
etl('../data/good-xss-200000.txt',x,0)
#etl('xss-200000.txt',x,1)
#etl('good-xss-200000.txt',x,0)



x_train, x_test, y_train, y_test = cross_validation.train_test_split(x,y, test_size=0.4, random_state=0)

clf = svm.SVC(kernel='linear', C=1).fit(x_train, y_train)

y_pred = clf.predict(x_test)
#print y_train
#print y_pred
#print y_test
do_metrics(y_test, y_pred)

#print clf.score(x_test, y_test)

#joblib.dump(clf,"xss-svm-200000-module.m")

'''
with open("good-xss-200000.txt") as f:
 for line in f:
#clf.predict([[2., 2.]])
 predict=clf.predict(get_feature(line))
 if predict == 1:
 print("maybe guest error xss %s") % (line)
'''