def write_signature_features_CSV(feature_list, file_name=""):
"""
write features for signature filter to csv
"""
if not file_name:
file_name = SIGNATURE_FEATURE_DATABASE
id = 0
with open(DATA_PATH + file_name, 'w') as file:
writer = csv.writer(file, delimiter=',')
writer.writerow(SIGNATURE_FEATURE_LIST_COLUMN_NAMES)
for entry in feature_list:
if not entry == None:
writer.writerow([
id, entry.url, entry.final_url, entry.label,
entry.cert_subject, entry.ent1, entry.ent2, entry.ent3,
entry.ent4, entry.ent5, entry.term1, entry.term2,
entry.term3, entry.term4, entry.term5
])
id += 1
log(action_logging_enum=INFO,
logging_text="Feature list written to CSV file. [{f}]".format(
f=file_name))
def do_search(domain, suffix, url, words):
search_string = ""
search_string = "{d}.{s}".format(d=domain, s=suffix)
for word in words:
if not word == None:
search_string += " {w}".format(w=word)
try:
result = bingsearch.search(search_string, wait_after_429=False)
except Exception as e:
result = -1
log(action_logging_enum=WARNING, logging_text=str(e))
if result == -1:
return None
for entry in result:
extracted_search = get_url_components(str(entry))
search_domain = extracted_search[3]
search_cctld = extracted_search[4]
found_url = str(entry)
if str(search_domain).__eq__(domain) and str(suffix).__eq__(str(search_cctld)) :
log(action_logging_enum=INFO,
logging_text="Login page found by search engine.")
return found_url, True
return url, False
def save_model(knn):
with open(SAVED_MODEL_FILE, 'wb') as file:
pickle.dump(knn, file)
log(action_logging_enum=INFO,
logging_text="[K-NEAREST NEIGHBOR]: Model saved.")
def load_model():
# load model from file
logistic_regression = pickle.load(open(SAVED_MODEL_FILE, 'rb'))
log(action_logging_enum=INFO, logging_text="[LOGISTIC REGRESSION]: Model loaded.")
return logistic_regression
def train_model(do_optimize=False, data=pd.DataFrame()):
log_module_start(MODULE_NAME=MODEL_NAME)
pd.set_option('display.max_columns', None)
if len(data) == 0:
data = pd.DataFrame(pd.read_csv(DATA_PATH + LEXICAL_FEATURE_DATABASE))
data = transform_data(data)
data = data.replace("True", 1)
data = data.replace("False", 0)
y = data['Label'].copy()
x = data.drop(["Label"], axis=1).copy()
train, test = train_test_split(data, test_size=0.35)
log(action_logging_enum=INFO,
logging_text="[K-NEAREST NEIGHBOR] Data ready for use.")
y_train = train['Label'].copy()
x_train = train.drop(["Label"], axis=1).copy()
params = {'n_neighbor': 9}
knn = KNeighborsClassifier() # params)
f1 = print_scores(knn, x, y)
log(action_logging_enum=INFO,
logging_text="[K-NEAREST NEIGHBOR] Starting training.")
knn.fit(x_train, y_train)
save_model(knn=knn)
log_module_complete(MODULE_NAME=MODEL_NAME)
return f1
def neighborhood_entropy(r, s=None, union=False):
# get neighborhood entropy for subsets R and S or UNION of R and S
feature_set = r
sum_list = []
if union:
feature_set = pd.concat([feature_set, s], axis=1)
n = len(feature_set)
def do_get(i_):
g_e = get_neighborhood(feature_set, i_, size_row, distance,
feature_vec)
return math.log((g_e / n), 2)
try:
feature_vec = feature_set.values.tolist()
size_row = len(feature_set)
distance = 0.15
for i in range(n):
sum_list.append(do_get(i))
except Exception as e:
log(ERROR, str(e))
sum = 0
for i in sum_list:
sum += i
return (sum * -(1 / n))
def predict_url(url):
try:
if isinstance(url, str):
features = mod_feature_extraction.extract_features_from_URL(url, "PREDICT", True)
x_pred = pd.DataFrame(features)
else:
x_pred = url
x_pred = transform_data(x_pred)
random_forest = load_model()
y_pred = random_forest.predict(x_pred)
return y_pred.tolist()
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
def check_status():
nonlocal complete
nonlocal failed
nonlocal size_before
while True:
entry = q.get()
url = entry.url
is_up = False
try:
response = requests.get(url, timeout=10, headers=headers)
response.raise_for_status()
if response.status_code >= 200 and response.status_code < 400:
is_up = True
except Exception as e:
log(action_logging_enum=ERROR, logging_text=str(e))
is_up = False
if is_up:
complete += 1
log(action_logging_enum=INFO,
logging_text="Found {} of {}. (Failed: {})".format(
complete, size_before, failed))
modify_list(entry)
else:
failed += 1
q.task_done()
def extract_features_from_signature_list(data):
"""
extract all signature features from list with urls and labels
"""
@ray.remote
def get_feature_entry(entry):
new_entry = extract_features_from_signature(entry.url, entry.label)
if not new_entry == None:
return new_entry
try:
result_ids = []
for entry in data:
result_ids.append(get_feature_entry.remote(entry))
feature_list = ray.get(result_ids)
except KeyboardInterrupt as e:
log(action_logging_enum=ERROR,
logging_text=
"Process interrupted by keyboard signal. Returning the list.")
feature_list = ray.get(result_ids)
return feature_list
return feature_list
def save_model(adaptive_boosting):
with open(SAVED_MODEL_FILE, 'wb') as file:
pickle.dump(adaptive_boosting, file)
log(action_logging_enum=INFO,
logging_text="[ADAPTIVE BOOSTING]: Model saved.")
def extract_features_from_website_list_ray(data):
"""
extract all features from website list, ray used for parallelism
"""
@ray.remote
def get_feature_entry(entry):
new_entry = extract_features_from_website(entry.url, entry.label,
False)
if not new_entry == None:
return new_entry
try:
result_ids = []
for entry in data:
result_ids.append(get_feature_entry.remote(entry))
feature_list = ray.get(result_ids)
except KeyboardInterrupt as e:
log(action_logging_enum=ERROR,
logging_text=
"Process interrupted by keyboard signal. Returning the list.")
feature_list = ray.get(result_ids)
return feature_list
return feature_list
def create_brand_list_by_alexa():
alexa_list = open_dataset_XML_file(filename=ALEXA_FILE,
iterateable="entry",
label="Benign",
url_label="url",
label_label="label")
brand_list = []
for entry in alexa_list:
url = entry.url
brand_list.append(tldextract.extract(url).domain)
root = et.Element("data")
tree = et.ElementTree(root)
for i in range(len(brand_list)):
entry = et.SubElement(root, "entry")
entrylabel = et.SubElement(entry, "brandname")
entrylabel.text = str(brand_list[i])
tree.write(open(DATA_PATH + BRAND_FILE, 'wb'), pretty_print=True)
log(action_logging_enum=INFO,
logging_text="Write process to XML finished for [{f}].".format(
f=BRAND_FILE))
def train_model(do_optimize=False, data=pd.DataFrame()):
log_module_start(MODULE_NAME=MODEL_NAME)
if len(data) == 0:
data = pd.read_csv(DATA_PATH + LEXICAL_FEATURE_DATABASE)
data = transform_data(data)
train, test = train_test_split(data, test_size=0.2)
pd.set_option('display.max_columns', None)
y = data['Label']
x = data.drop(['Label'], axis=1).values
log(action_logging_enum=INFO,
logging_text="[ADAPTIVE BOOSTING] Data ready for use.")
if do_optimize == True:
optimize()
params = {'n_estimators': 120, 'random_state': 0}
log(action_logging_enum=INFO,
logging_text="[ADAPTIVE BOOSTING] Starting training.")
adaptive_boosting = AdaBoostClassifier() #params)
f1 = print_scores(adaptive_boosting, x, y)
y_train = train['Label']
x_train = train.drop(['Label'], axis=1)
# random_state=1, n_estimators=120, min_samples_leaf=5, min_samples_split=10,
# max_features='sqrt', max_depth=17
adaptive_boosting.fit(x_train, y_train)
save_model(adaptive_boosting=adaptive_boosting)
log_module_complete(MODULE_NAME=MODEL_NAME)
return f1
def optimize():
log(action_logging_enum=INFO,
logging_text="[SUPPORT VECTOR MACHINE]: Starting to search for best c, gamma, kernel by cross validating different values.")
# read data
train = pd.read_csv(DATA_PATH + LEXICAL_FEATURE_DATABASE)
train = transform_data(train)
x_train = train.drop(['Label'], axis=1)
y_train = train['Label'].copy()
param_grid = [
{'C': [1, 10, 100, 1000], 'kernel': ['linear']},
{'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001], 'kernel': ['rbf']},
]
# Create grid search using 5-fold cross validation
model = SVC()
clf = GridSearchCV(model, param_grid, cv=10, verbose=0)
best_model = clf.fit(x_train, y_train)
# View best hyperparameters
log(action_logging_enum=INFO, logging_text="[SUPPORT VECTOR MACHINE]: Hyperparameter tuning completed.")
log(INFO, str('Best Kernel:', best_model.best_estimator_.get_params()['kernel']))
log(INFO, str('Best C:', best_model.best_estimator_.get_params()['C']))
log(INFO, str('Best gamma:', best_model.best_estimator_.get_params()['gamma']))
def save_model(extreme_gradient):
with open(SAVED_MODEL_FILE, 'wb') as file:
pickle.dump(extreme_gradient, file)
log(action_logging_enum=INFO,
logging_text="[EXTREME GRADIENT BOOSTING]: Model saved.")
return
def load_model():
# load model from file
support_vector_machine = pickle.load(open(SAVED_MODEL_FILE, 'rb'))
log(action_logging_enum=INFO, logging_text="[SUPPORT VECTOR MACHINE]: Model loaded.")
return support_vector_machine
def load_model():
# load model from file
decision_tree = pickle.load(open(SAVED_MODEL_FILE, 'rb'))
log(action_logging_enum=INFO,
logging_text="[DECISION TREE]: Model loaded.")
return decision_tree
def get_feature_contribution_subset(feature_, y_, coalition_, coal_size):
# get contribution to coalition for feature_ (delta for feature i and coalition K_i)
mutual_information = neighbor_mutual_information_coalition(
coalition_, y_, feature_)
if not mutual_information >= 0:
log(INFO, "Return contribution.")
return 0
log(INFO, "Contribution bigger than 0.")
# check for all features S in coalition and feature_ R -> NMI(S ; D | R) > NMI(S ; D)
related = 0
is_related = False
for i in range(coal_size):
nmi_cj_y_ci = neighbor_mutual_information_coalition(
coalition_.iloc[:, i], y_, feature_)
nmi_cj_y = neighbor_mutual_information_coalition(
coalition_.iloc[:, i], y_, less=True)
if nmi_cj_y_ci > nmi_cj_y:
related += 1
if related >= (coal_size / 2):
log(INFO, "Contribution is related.")
is_related = True
if not is_related:
log(INFO, "Return contribution.")
return 0
log(INFO, "Return contribution.")
return 1
def get_input_to_lower():
prompt = input("type a command:")
prompt = prompt.lower()
# log system commands
if prompt != "":
log(action_logging_enum=INFO, logging_text="--> " + prompt)
return prompt
def do_get(i_, y, x, combinations, feature, c, lene):
res = get_feature_contribution_subset(y_=y,
coalition_=x.iloc[:,
combinations],
feature_=feature,
coal_size=len(combinations))
log(INFO, "Processed coalition {} of {}.".format(c, lene))
return res
def load_model():
try:
# load model from file
random_forest = pickle.load(open(SAVED_MODEL_FILE, 'rb'))
log(action_logging_enum=INFO, logging_text="[RANDOM FOREST]: Model loaded.")
return random_forest
except Exception:
return None
def load_model():
# load model from file
try:
adaptive_boosting = pickle.load(open(SAVED_MODEL_FILE, 'rb'))
except Exception:
return None
log(action_logging_enum=INFO, logging_text="[ADAPTIVE BOOSTING]: Model loaded.")
return adaptive_boosting
def load_model():
# load model from file
try:
extreme_gradient = pickle.load(open(SAVED_MODEL_FILE, 'rb'))
except Exception:
return None
log(action_logging_enum=INFO,
logging_text="[EXTREME GRADIENT BOOSTING]: Model loaded.")
return extreme_gradient
def open_dataset_XML_file(filename,
iterateable,
label=None,
url_label="url",
label_label=None,
max_line_count=-1):
"""
open xml file and write to list
filename: xml filename
iterateable: node containing data
label: label for all data
url_label: url specifier in xml data
label_label: label specifiert in xml data
max_line_count: maximum entries to be written to list
"""
PATH = ""
if not os.path.isfile(DATA_PATH + filename):
log(action_logging_enum=WARNING,
logging_text="File [{f}] does not exist.".format(f=filename))
log(action_logging_enum=INFO, logging_text="Trying in backup folder.")
if not os.path.isfile(DATA_BACKUP_PATH + filename):
log(action_logging_enum=ERROR,
logging_text="File [{f}] does even not exist in backup folder."
.format(f=filename))
return None
else:
PATH = DATA_BACKUP_PATH
log(action_logging_enum=INFO,
logging_text="Found in backup folder.")
else:
PATH = DATA_PATH
datalist = []
parser = et.XMLParser(strip_cdata=False)
xtree = et.parse(PATH + filename, parser=parser)
root = xtree.getroot()
index = 1
for entry in root.iter(iterateable):
url = entry.find(url_label).text
e = Entry(entry.find(label_label).text,
url) if not (label_label == None) else Entry(label, url)
datalist.append(e)
if index == max_line_count:
break
index += 1
log(action_logging_enum=INFO,
logging_text="XML File filled in list. FILE: [{f}].".format(
f=filename))
return datalist
def load_model():
# load model from file
try:
knn = pickle.load(open(SAVED_MODEL_FILE, 'rb'))
except Exception:
return None
log(action_logging_enum=INFO,
logging_text="[K-NEAREST NEIGHBOR]: Model loaded.")
return knn
def delete_data(filename):
"""
delete file for param filename in data_path
"""
if os.path.isfile(DATA_PATH + filename):
os.remove(DATA_PATH + filename)
log(action_logging_enum=INFO,
logging_text="File [{f}] deleted.".format(f=DATA_PATH + filename))
else:
log(action_logging_enum=WARNING,
logging_text="File [{f}] dose not exists.".format(f=filename))
def save_last_score(prec_train, prec_test, recall_train, recall_test, f1_train, f1_test):
with open(SCORE_FILE, 'w') as file:
file.write("Precision Train: " + str(prec_train) + "\n")
file.write("Precision Test: " + str(prec_test) + "\n")
file.write("Recall Train: " + str(recall_train) + "\n")
file.write("Recall Test: " + str(recall_test) + "\n")
file.write("F1-Score Train: " + str(f1_train) + "\n")
file.write("F1-Score Test: " + str(f1_test) + "\n")
file.close()
log(action_logging_enum=INFO, logging_text="[ADAPTIVE BOOSTING]: score saved in [{f}]".format(f=SCORE_FILE))
return
def download_file(url, filename):
"""
download database file from url and save by filename
url: url where data has to be downloaded
filename: filename for data to be saved
"""
response = requests.get(url, timeout=10, headers=headers)
with open(DATA_PATH + filename, 'wb') as file:
file.write(response.content)
log(action_logging_enum=INFO,
logging_text="Download completed for [{f}].".format(f=filename))
def merge_dataframes(dataframe):
try:
nonlocal df
if not pd.DataFrame(dataframe).empty:
if df.empty:
df = dataframe
else:
df = pd.concat([df, dataframe], ignore_index=True)
except Exception as e:
log(action_logging_enum=WARNING, logging_text=str(e))
def fetch_results(search_term, number_results, language_code):
escaped_search_term = search_term.replace(' ', '+')
google_url = 'https://www.google.de/search?q={}&num={}&hl={}'.format(
escaped_search_term, number_results + 1, language_code)
try:
response = requests.get(google_url, headers=usr_agent, timeout=10)
response.raise_for_status()
except Exception as e:
try:
time.sleep(2)
response = requests.get(google_url,
headers=usr_agent,
timeout=10)
except Exception as e:
log(action_logging_enum=ERROR,
logging_text=
"[Function search]: An error occured while querying the Google API."
)
log(action_logging_enum=INFO,
logging_text="[Function search]: Error description: {err}".
format(err=str(e)))
return -1
status_code = int(response.status_code)
if status_code >= 200 and status_code < 400:
return response.content
else:
if wait_after_429:
while status_code < 200 or status_code >= 400:
log(action_logging_enum=WARNING,
logging_text="Google Search returned Status Code 429. "
"Next check in: 5 minutes.")
time.sleep(300)
try:
response = requests.get(google_url,
headers=usr_agent,
timeout=10)
status_code = int(response.status_code)
except Exception as e:
log(action_logging_enum=ERROR,
logging_text=
"An error occured while querying the Google API.")
log(action_logging_enum=INFO,
logging_text="Error description: {err}".format(
err=str(e)))
return response.content
return -1
