def do_analyze(self, type=None):
# Create objects
if K.backend() == "tensorflow":
with tf.Session(graph = tf.Graph()) as sess:
model, configuration = self.build_model(type=type)
# First create the sources of data
data_helpers = DataHelpers(data_source=configuration['paths'],
label=None,
tokens_per_line=configuration['tokens_per_line'],
number_lines=configuration['number_lines'],
samples_per_batch=configuration['samples_per_batch'],
seed=configuration['seed'])
# Get the data sources
online_generator = data_helpers.get_data_stream(configuration['vocabulary'],
configuration['input_queue'])
logging.info("Convolutional intrusion detection: %s" % type)
result = model.analyze_stream(online_generator,self.output_queue)
else:
model, configuration = self.build_model(type=type)
# First create the sources of data
data_helpers = DataHelpers(data_source=configuration['paths'],
label=None,
tokens_per_line=configuration['tokens_per_line'],
number_lines=configuration['number_lines'],
samples_per_batch=configuration['samples_per_batch'],
seed=configuration['seed'])
# Get the data sources
online_generator = data_helpers.get_data_stream(configuration['vocabulary'],
configuration['input_queue'])
logging.info("Convolutional intrusion detection: %s" % type)
result = model.analyze_stream(online_generator,self.output_queue)
def analyze_stream(self, data_source, max_length, n_gram, output_queue):
self.output_queue = output_queue
data_helpers = DataHelpers(data_source,
None,
max_length,
n_gram,
samples_per_batch=None,
seed=20)
data_generator = data_helpers.get_data_stream(self.vocabulary,
data_source)
while True:
data = next(data_generator)
result = self.model.predict(data)
self.output_queue.put(result)
