def runIDS(verbose=False):
print("Starting IDS...")
try:
# Create log file if it does not exist.
if not os.path.exists(r'logs\idslogs\ids.log'):
file = open(os.path.join(r'logs\idslogs', 'ids.log'), 'w')
file.close()
logging.basicConfig(filename=os.path.join(r'logs\idslogs', 'ids.log'),
level=logging.INFO)
csvloader = CSVFlowLoader(os.path.join(CSVFILEPATH, csvfilename))
mlengine = MLEngine(MODEL_FILEPATH, DATACLEAN_PIPELINE_FILEPATH,
DATAPREP_PIPELINE_FILEPATH)
while True:
for flowline in csvloader.tailFile():
csValsArray = [list(flowline.split(","))]
csValsDF = pd.DataFrame(csValsArray, columns=columns)
# Actual detection and printing results out in stdout.
if mlengine.predict(csValsDF)[0] == PredictLabel.ANOMALY.value:
print("ANOMALY: %s" % (parsePredictionDF(csValsDF)))
logging.info("ANOMALY: %s" % (parsePredictionDF(csValsDF)))
else:
if verbose:
print("BENIGN: %s" % (parsePredictionDF(csValsDF)))
except KeyboardInterrupt:
print("Exiting...")
csvloader.destroy()
def test_predict(self):
columns = constants.COLUMNS
mlengine = MLEngine()
# resultSet = [-1,1]
# inputData = [[49188,4,2,0,12,0,6,6,6,0,0,0,0,0,14000,3500,4,0,4,4,4,4,0,4,4,0,0,0,0,0,0,0,0,0,40,0,500000,0,6,6,6,0,0,0,0,0,0,1,1,0,0,0,9,6,0,40,0,0,0,0,0,0,2,12,0,0,329,-1,1,20,0,0,0,0,0,0,0,0]]
inputData = [[
'192.168.91.225-13.107.4.52-65026-80-6', '192.168.91.225', 65026,
'13.107.4.52', 80, 6, '30/09/2019 07:34:49 PM', 19002, 2, 5, 111.0,
1009.0, 111.0, 0.0, 55.5, 78.48885271170677, 1009.0, 0.0, 201.8,
451.2385178594576, 58941.164087990735, 368.3822755499421, 3167.0,
3013.9242857112386, 7587.0, 135.0, 543.0, 543.0, 0.0, 543.0, 543.0,
19002.0, 4750.5, 2930.5069527301926, 8265.0, 1358.0, 0, 0, 0, 0,
40, 124, 105.25207872855488, 263.1301968213872, 0.0, 1009.0, 140.0,
353.2708390375375, 124800.28571428571, 0, 1, 0, 0, 0, 0, 0, 0, 2.0,
160.0, 55.5, 201.8, 0, 0, 0, 0, 0, 0, 2, 111, 5, 1009, -1, 115, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 'No Label'
]]
prep_inputData = pd.DataFrame(inputData, columns=columns)
prediction = mlengine.predict(prep_inputData)
# print(prediction)
self.assertEquals(prediction, [-1])
# if __name__ == '__main__':
# unittest.main()
