def predict_url(url):
try:
features = mod_feature_extraction.extract_features_from_website(url, "PREDICT", True)
x_pred = pd.DataFrame(features)
x_pred = x_pred.drop(["Label", "ID", "URL"], axis=1)
y_pred = adaptive_boosting_pre_loaded.predict(x_pred)
result = "NO RESULT"
int_result = 0
if str(y_pred[0]) == "0":
result = "Benign"
if str(y_pred[0]) == "1":
result = "Phish"
except Exception as e:
exc_type, exc_obj, tb = sys.exc_info()
f = tb.tb_frame
lineno = tb.tb_lineno
filename = f.f_code.co_filename
linecache.checkcache(filename)
line = linecache.getline(filename, lineno, f.f_globals)
log(INFO, 'EXCEPTION IN ({}, LINE {} "{}"): {}'.format(filename, lineno, line.strip(), exc_obj))
log(action_logging_enum=WARNING, logging_text=str(e))
log(action_logging_enum=WARNING, logging_text=str(e.__traceback__))
return 0
log(action_logging_enum=INFO, logging_text="[ADAPTIVE BOOSTING]: Result for URL [{u}] = {l}".format(u=str(url), l=result))
return result
def predict_url(url):
support_vector_machine = load_model()
x_pred = pd.DataFrame(extract_features_from_website(url, "PREDICT", True))
x_pred = transform_data(x_pred)
y_pred = support_vector_machine.predict(x_pred)
result = "NO RESULT"
if str(y_pred[0]) == "0":
result = "Benign"
if str(y_pred[0]) == "1":
result = "Phish"
log(action_logging_enum=INFO,
logging_text="[SUPPORT VECTOR MACHINE]: Result for URL [{u}] = {l}".format(u=url, l=result))
return result
def predict_url(url):
logistic_regression = load_model()
x_pred = pd.DataFrame(extract_features_from_website(url, "PREDICT", True))
x_pred = transform_data(x_pred)
y_pred = logistic_regression.predict(x_pred)
result = "NO RESULT"
if str(y_pred[0]) == "0":
result = "Benign"
if str(y_pred[0]) == "1":
result = "Phish"
log(action_logging_enum=INFO,
logging_text="[LOGISTIC REGRESSION]: Result for URL [{u}] = {l}".
format(u=url, l=result))
return result
def predict_url(url):
decision_tree = load_model()
x_pred = pd.DataFrame(extract_features_from_website(url, "PREDICT", True))
x_pred = transform_data(x_pred)
y_pred = decision_tree.predict(x_pred)
result = "NO RESULT"
if str(y_pred[0]) == "0":
result = "Benign"
if str(y_pred[0]) == "1":
result = "Phish"
log(action_logging_enum=INFO,
logging_text="[DECISION TREE]: Result for URL [{u}] = {l}".format(
u=url, l=result))
return result
def predict_url(url):
knn = load_model()
x_pred = pd.DataFrame(extract_features_from_website(url, "PREDICT", True))
x_pred = transform_data(x_pred)
y_pred = knn.predict(x_pred)
result = "NO RESULT"
if str(y_pred[0]) == "0":
result = "Benign"
if str(y_pred[0]) == "1":
result = "Phish"
log(action_logging_enum=INFO,
logging_text="[K-NEAREST NEIGHBOR]: Result for URL [{u}] = {l}".format(
u=url, l=result))
def predict_url(url):
try:
features = mod_feature_extraction.extract_features_from_website(
url, "PREDICT", True)
x_pred = pd.DataFrame(features)
for index, col in enumerate(x_pred.columns):
if x_pred[col].dtype == np.bool:
name = x_pred.iloc[:, index].name
x_pred[name] = x_pred[name].astype(int)
x_pred = x_pred.drop(["Label", "ID", "URL", "Final URL"], axis=1)
y_pred = extreme_gradient_pre_loaded.predict(x_pred)
result = "NO RESULT"
int_result = 0
if str(y_pred[0]) == "0":
result = "Benign"
if str(y_pred[0]) == "1":
result = "Phish"
int_result = 1
except Exception as e:
exc_type, exc_obj, tb = sys.exc_info()
f = tb.tb_frame
lineno = tb.tb_lineno
filename = f.f_code.co_filename
linecache.checkcache(filename)
line = linecache.getline(filename, lineno, f.f_globals)
log(
ERROR, 'EXCEPTION IN ({}, LINE {} "{}"): {}'.format(
filename, lineno, line.strip(), exc_obj))
log(action_logging_enum=WARNING, logging_text=str(e))
log(action_logging_enum=WARNING, logging_text=str(e.__traceback__))
return 0
log(action_logging_enum=INFO,
logging_text="[EXTREME GRADIENT BOOSTING]: Result for URL [{u}] = {l}".
format(u=str(url), l=result))
return int_result
def predict_url(url, proba=False):
try:
if isinstance(url, str):
features = mod_feature_extraction.extract_features_from_website(
url, "PREDICT", True)
x_pred = pd.DataFrame(features)
else:
x_pred = url
x_pred = x_pred[[
'Ratio Similarity', 'Ratio Description Sim', 'Number HREF',
'Number DIV', 'Number LI', 'Ratio Title Sim', 'Number Span',
'Number UL', 'Has Bond Status', 'Number Image',
'Ratio Copyright Sim', 'Number PhishyTokens',
'Number Extern Links', 'Number Button', 'Number Inputs',
'Number Paragr', 'Ratio Unique Links', 'Has Freq Domain Extern',
'Has Copyright', 'Has Button', 'Has Redirect', 'Has iFrame',
'Has Extern Content', 'Has Meta', 'Has Input', 'Number Option',
'Has Action', 'Number OL', 'Number TR', 'Has Hidden Element',
'Number Checkbox'
]]
if proba:
y_pred = random_forest_pre_loaded.predict_proba(x_pred)
else:
y_pred = random_forest_pre_loaded.predict(x_pred)
return y_pred
except Exception as e:
exc_type, exc_obj, tb = sys.exc_info()
f = tb.tb_frame
lineno = tb.tb_lineno
filename = f.f_code.co_filename
linecache.checkcache(filename)
line = linecache.getline(filename, lineno, f.f_globals)
print('EXCEPTION IN ({}, LINE {} "{}"): {}'.format(
filename, lineno, line.strip(), exc_obj))
log(action_logging_enum=WARNING, logging_text=str(e))
log(action_logging_enum=WARNING, logging_text=str(e.__traceback__))
return None