def OnPressEnter(self,event):
# self.labelVariable.set( self.entryVariable.get()+" (You pressed ENTER)" )
url = self.entryVariable.get()
try:
processed_url = process_url(url).values()
(pred, conf) = self.voter.vote(processed_url)
url_class = "Malicious" if pred == 1 else "Benign"
self.labelVariable.set("URL Classified as {}, Confidence: {:.4f}%".format(url_class, conf * 100))
except:
self.labelVariable.set("Not a valid URL")
self.entry.focus_set()
self.entry.selection_range(0, Tkinter.END)
def create_dataset(urls, label):
X, y = [], []
for url in urls:
url_dict = process_url(url)
values = []
for value in url_dict.itervalues():
values.append(value)
# print value
# print item.value
X.append(values)
y.append(label)
return np.array(X), np.array(y)
def create_dataset(urls, label):
X, y = [], []
for url in urls:
url_dict = process_url(url)
values = []
for value in url_dict.itervalues():
values.append(value)
# print value
# print item.value
X.append(values)
y.append(label)
return np.array(X), np.array(y)
def OnPressEnter(self, event):
# self.labelVariable.set( self.entryVariable.get()+" (You pressed ENTER)" )
url = self.entryVariable.get()
try:
processed_url = process_url(url).values()
(pred, conf) = self.voter.vote(processed_url)
url_class = "Malicious" if pred == 1 else "Benign"
self.labelVariable.set(
"URL Classified as {}, Confidence: {:.4f}%".format(
url_class, conf * 100))
except:
self.labelVariable.set("Not a valid URL")
self.entry.focus_set()
self.entry.selection_range(0, Tkinter.END)
# train = dataset.from_npy(path, 'train.npy')
# train_X, train_y = train[:, :-1], train[:, -1]
# voter.fit(train_X, train_y)
# save_voter(voter, path)
voter = load_voter(path)
test = dataset.from_npy(path, 'test.npy')
test_X, test_y = test[:, :-1], test[:, -1]
voter.confusion_matrix(test_X, test_y)
# for clf in voter._classifiers:
# print type(clf).__name__
# cm = confusion_matrix(test_y, clf.predict(test_X))
# plot_confusion_matrix(cm)
# plt.draw()
#
test_urls = [
'https://www.youtube.com/watch?v=4WM6hB7l4Lc&list=PLQVvvaa0QuDd0flgGphKCej-9jp-QdzZ3&index=12&feature=iv&src_vid=81ZGOib7DTk&annotation_id=annotation_1856532697',
'http://ld.mediaget.com/index.php?l=ru&fu=http:/www.playground.ru/download/?cheat=grand_theft_auto_4_gta_iv_episodes_from_liberty_city_eflc_sohranenie_100-41709&r=playground.ru&f=grand_theft_auto_4_gta_iv_episodes_from_liberty_city_eflc__&%23x421;&%23x43e;&%23x445;&%23x440;&%23x430;&%23x43d;&%23x435;&%23x43d;&%23x438;&%23x435;_100%25',
'https://raw.github.com/inquisb/shellcodeexec/master/windows/shellcodeexec.x32.exe',
'http://www.ezthemes.com/site_advertisers/extrafindWD.exe'
]
for test_url in test_urls:
processed_url = process_url(test_url).values()
print voter.vote(processed_url)
# break
# winner, conf = voter.vote(processed_url)
# print 'Classification:', 'Malicious' if winner == 1 else 'Benign', 'Confidence:', conf * 100
#
# plt.show()
#
# train = dataset.from_npy(path, 'train.npy')
# train_X, train_y = train[:, :-1], train[:, -1]
# voter.fit(train_X, train_y)
# save_voter(voter, path)
voter = load_voter(path)
test = dataset.from_npy(path, 'test.npy')
test_X, test_y = test[:, :-1], test[:, -1]
voter.confusion_matrix(test_X, test_y)
# for clf in voter._classifiers:
# print type(clf).__name__
# cm = confusion_matrix(test_y, clf.predict(test_X))
# plot_confusion_matrix(cm)
# plt.draw()
#
test_urls = [
'https://www.youtube.com/watch?v=4WM6hB7l4Lc&list=PLQVvvaa0QuDd0flgGphKCej-9jp-QdzZ3&index=12&feature=iv&src_vid=81ZGOib7DTk&annotation_id=annotation_1856532697',
'http://ld.mediaget.com/index.php?l=ru&fu=http:/www.playground.ru/download/?cheat=grand_theft_auto_4_gta_iv_episodes_from_liberty_city_eflc_sohranenie_100-41709&r=playground.ru&f=grand_theft_auto_4_gta_iv_episodes_from_liberty_city_eflc__&%23x421;&%23x43e;&%23x445;&%23x440;&%23x430;&%23x43d;&%23x435;&%23x43d;&%23x438;&%23x435;_100%25',
'https://raw.github.com/inquisb/shellcodeexec/master/windows/shellcodeexec.x32.exe',
'http://www.ezthemes.com/site_advertisers/extrafindWD.exe']
for test_url in test_urls:
processed_url = process_url(test_url).values()
print voter.vote(processed_url)
# break
# winner, conf = voter.vote(processed_url)
# print 'Classification:', 'Malicious' if winner == 1 else 'Benign', 'Confidence:', conf * 100
#
# plt.show()