def get_the_model_of_a_connection(self, group_of_model_id, connection_id):
""" Given a connection_id and group of model id, get the model """
# Get the note id, group_id and group
dataset_id = int(group_of_model_id.split('-')[0])
dataset = __datasets__.get_dataset(dataset_id)
group = __groupofgroupofmodels__.get_group(group_of_model_id)
try:
if group.has_model(connection_id):
model = group.get_model(connection_id)
return model
except AttributeError:
print_error('The connection does not have a model. Probably deleted.')
return False
def delete_group_of_connections(self, conn_id):
group = self.get_group(conn_id)
if group:
# First delete all the connections inside the group of connections
group.delete_all_connections()
# Now delete the group of connections
self.group_of_connections.pop(conn_id)
print_info('Deleted group of connections with id {}'.format(conn_id))
# Delete the reference to this group from the dataset
dataset = __datasets__.get_dataset(conn_id)
dataset.remove_group_of_connections_id(conn_id)
else:
print_error('No such group of connections exists.')
def delete_group_of_connections(self, conn_id):
group = self.get_group(conn_id)
if group:
# First delete all the connections inside the group of connections
group.delete_all_connections()
# Now delete the group of connections
self.group_of_connections.pop(conn_id)
print_info('Deleted group of connections with id {}'.format(conn_id))
# Delete the reference to this group from the dataset
dataset = __datasets__.get_dataset(conn_id)
dataset.remove_group_of_connections_id(conn_id)
else:
print_error('No such group of connections exists.')
def get_the_model_of_a_connection(self, connection_id):
""" Given a connection_id and current dataset, get the model """
if __datasets__.current:
# Get the note id, group_id and group
dataset = __datasets__.get_dataset(__datasets__.current.get_id())
group_of_models = dataset.get_group_of_models()
for group_id in group_of_models: # Usually is only one group...
group = __groupofgroupofmodels__.get_group(group_id)
if group.has_model(connection_id):
model = group.get_model(connection_id)
return model
else:
print_error('There is no dataset selected.')
def get_the_model_of_a_connection(self, group_of_model_id, connection_id):
""" Given a connection_id and group of model id, get the model """
# Get the note id, group_id and group
dataset_id = int(group_of_model_id.split('-')[0])
dataset = __datasets__.get_dataset(dataset_id)
group = __groupofgroupofmodels__.get_group(group_of_model_id)
try:
if group.has_model(connection_id):
model = group.get_model(connection_id)
return model
except AttributeError:
print_error(
'The connection does not have a model. Probably deleted.')
return False
def list_groups(self):
print_info('Groups of Models')
# If we selected a dataset, just print the one belonging to the dataset
if __datasets__.current:
rows = []
for group in self.group_of_models.values():
if group.get_dataset_id() == __datasets__.current.get_id():
rows.append([group.get_id(), len(group.get_models()), __datasets__.current.get_id(), __datasets__.current.get_name() ])
print(table(header=['Group of Model Id', 'Amount of Models', 'Dataset Id', 'Dataset Name'], rows=rows))
# Otherwise print them all
else:
rows = []
for group in self.group_of_models.values():
# Get the dataset based on the dataset id stored from this group
dataset = __datasets__.get_dataset(group.get_dataset_id())
rows.append([group.get_id(), len(group.get_models()), dataset.get_id(), dataset.get_name() ])
print(table(header=['Group of Model Id', 'Amount of Models', 'Dataset Id', 'Dataset Name'], rows=rows))
def list_groups(self):
print_info('Groups of Models')
# If we selected a dataset, just print the one belonging to the dataset
if __datasets__.current:
rows = []
for group in self.group_of_models.values():
if group.get_dataset_id() == __datasets__.current.get_id():
rows.append([group.get_id(), group.get_constructor_id(), len(group.get_models()), __datasets__.current.get_id(), __datasets__.current.get_name() ])
print(table(header=['Group of Model Id', 'Constructor ID', 'Amount of Models', 'Dataset Id', 'Dataset Name'], rows=rows))
# Otherwise print them all
else:
rows = []
for group in self.group_of_models.values():
# Get the dataset based on the dataset id stored from this group
dataset = __datasets__.get_dataset(group.get_dataset_id())
rows.append([group.get_id(), group.get_constructor_id(), len(group.get_models()), dataset.get_id(), dataset.get_name() ])
print(table(header=['Group of Model Id', 'Constructor ID', 'Amount of Models', 'Dataset Id', 'Dataset Name'], rows=rows))
def visualize_dataset(self, dataset_id, multiplier, filter):
# Get the netflow file
self.dataset = __datasets__.get_dataset(dataset_id)
try:
self.binetflow_file = self.dataset.get_file_type('binetflow')
except AttributeError:
print_error('That testing dataset does no seem to exist.')
return False
# Open the file
try:
file = open(self.binetflow_file.get_name(), 'r')
self.setup_screen()
except IOError:
print_error('The binetflow file is not present in the system.')
return False
# construct filter
self.construct_filter(filter)
# Clean the previous models from the constructor
__modelsconstructors__.get_default_constructor().clean_models()
# Remove the header
header_line = file.readline().strip()
# Find the separation character
self.find_separator(header_line)
# Extract the columns names
self.find_columns_names(header_line)
line = ','.join(file.readline().strip().split(',')[:14])
#logfile = open('log','w')
while line:
# Using our own extract_columns function makes this module more independent
column_values = self.extract_columns_values(line)
# Extract its 4-tuple. Find (or create) the tuple object
tuple4 = column_values['SrcAddr']+'-'+column_values['DstAddr']+'-'+column_values['Dport']+'-'+column_values['Proto']
# Get the _local_ model. We don't want to mess with the real models in the database, but we need the structure to get the state
model = self.get_model(tuple4)
# filter
if not self.apply_filter(tuple4):
line = ','.join(file.readline().strip().split(',')[:14])
continue
if not model:
model = Model(tuple4)
self.set_model(model)
constructor_id = __modelsconstructors__.get_default_constructor().get_id()
# Warning, here we depend on the modelsconstrutor
model.set_constructor(__modelsconstructors__.get_constructor(constructor_id))
flow = Flow(0) # Fake flow id
flow.add_starttime(column_values['StartTime'])
flow.add_duration(column_values['Dur'])
flow.add_proto(column_values['Proto'])
flow.add_scraddr(column_values['SrcAddr'])
flow.add_dir(column_values['Dir'])
flow.add_dstaddr(column_values['DstAddr'])
flow.add_dport(column_values['Dport'])
flow.add_state(column_values['State'])
flow.add_stos(column_values['sTos'])
flow.add_dtos(column_values['dTos'])
flow.add_totpkts(column_values['TotPkts'])
flow.add_totbytes(column_values['TotBytes'])
try:
flow.add_srcbytes(column_values['SrcBytes'])
except KeyError:
# It can happen that we don't have the SrcBytes column
pass
try:
flow.add_srcUdata(column_values['srcUdata'])
except KeyError:
# It can happen that we don't have the srcUdata column
pass
try:
flow.add_dstUdata(column_values['dstUdata'])
except KeyError:
# It can happen that we don't have the dstUdata column
pass
try:
flow.add_label(column_values['Label'])
except KeyError:
# It can happen that we don't have the label column
pass
# Add the flow
model.add_flow(flow)
# As fast as we can or with some delay?
if multiplier != 0.0 and multiplier != -1:
# Wait some time between flows.
last_time = model.get_last_flow_time()
current_time = datetime.strptime(column_values['StartTime'], '%Y/%m/%d %H:%M:%S.%f')
if last_time:
diff = current_time - last_time
wait_time = diff.total_seconds()
time.sleep(wait_time / multiplier)
model.add_last_flow_time(current_time)
# Wait the necessary time. After the visualization
elif multiplier == -1:
time.sleep(0.1)
# Visualize this model
self.qscreen.put(model)
line = ','.join(file.readline().strip().split(',')[:14])
self.qscreen.put('Stop')
file.close()
def visualize_dataset(self, dataset_id, multiplier, filter):
# Get the netflow file
self.dataset = __datasets__.get_dataset(dataset_id)
try:
self.binetflow_file = self.dataset.get_file_type('binetflow')
except AttributeError:
print_error('That testing dataset does no seem to exist.')
return False
# Open the file
try:
file = open(self.binetflow_file.get_name(), 'r')
self.setup_screen()
except IOError:
print_error('The binetflow file is not present in the system.')
return False
# construct filter
self.construct_filter(filter)
# Clean the previous models from the constructor
__modelsconstructors__.get_default_constructor().clean_models()
# Remove the header
header_line = file.readline().strip()
# Find the separation character
self.find_separator(header_line)
# Extract the columns names
self.find_columns_names(header_line)
line = ','.join(file.readline().strip().split(',')[:14])
#logfile = open('log','w')
while line:
# Using our own extract_columns function makes this module more independent
column_values = self.extract_columns_values(line)
# Extract its 4-tuple. Find (or create) the tuple object
tuple4 = column_values['SrcAddr']+'-'+column_values['DstAddr']+'-'+column_values['Dport']+'-'+column_values['Proto']
# Get the _local_ model. We don't want to mess with the real models in the database, but we need the structure to get the state
model = self.get_model(tuple4)
# filter
if not self.apply_filter(tuple4):
line = ','.join(file.readline().strip().split(',')[:14])
continue
if not model:
model = Model(tuple4)
self.set_model(model)
constructor_id = __modelsconstructors__.get_default_constructor().get_id()
# Warning, here we depend on the modelsconstrutor
model.set_constructor(__modelsconstructors__.get_constructor(constructor_id))
flow = Flow(0) # Fake flow id
flow.add_starttime(column_values['StartTime'])
flow.add_duration(column_values['Dur'])
flow.add_proto(column_values['Proto'])
flow.add_scraddr(column_values['SrcAddr'])
flow.add_dir(column_values['Dir'])
flow.add_dstaddr(column_values['DstAddr'])
flow.add_dport(column_values['Dport'])
flow.add_state(column_values['State'])
flow.add_stos(column_values['sTos'])
flow.add_dtos(column_values['dTos'])
flow.add_totpkts(column_values['TotPkts'])
flow.add_totbytes(column_values['TotBytes'])
try:
flow.add_srcbytes(column_values['SrcBytes'])
except KeyError:
# It can happen that we don't have the SrcBytes column
pass
try:
flow.add_srcUdata(column_values['srcUdata'])
except KeyError:
# It can happen that we don't have the srcUdata column
pass
try:
flow.add_dstUdata(column_values['dstUdata'])
except KeyError:
# It can happen that we don't have the dstUdata column
pass
try:
flow.add_label(column_values['Label'])
except KeyError:
# It can happen that we don't have the label column
pass
# Add the flow
model.add_flow(flow)
# As fast as we can or with some delay?
if multiplier != 0.0 and multiplier != -1:
# Wait some time between flows.
last_time = model.get_last_flow_time()
current_time = datetime.strptime(column_values['StartTime'], '%Y/%m/%d %H:%M:%S.%f')
if last_time:
diff = current_time - last_time
wait_time = diff.total_seconds()
time.sleep(wait_time / multiplier)
model.add_last_flow_time(current_time)
# Wait the necessary time. After the visualization
elif multiplier == -1:
time.sleep(0.1)
# Visualize this model
self.qscreen.put(model)
line = ','.join(file.readline().strip().split(',')[:14])
self.qscreen.put('Stop')
file.close()