def main():
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from utilities import frames_fetcher, temp_frame_extractor, bits_extractor,predictor
import concurrent.futures
all_ids = ['0CF00400', '0CF00300', '18FEF100', '1CFF6F00', '18ECFF00', '18FF8800', '18FF8400',
'18FEE500', '18F00029', '18FEF200', '18FF7F00', '1CFF7100', '18EBFF00', '18FF8200',
'18FF8600', '18FEDC00', '1CFF7700', '18FF8900', '18FEDF00', '18FEE900', '18FF8700',
'18FEE700', '1CFEB300', '18FEC100', '18FEEE00', '18ECFF29', '18EBFF29', '0C000027',
'0C000F27', '18FEF111', '0CF00203', '0CF00327', '18FF8327', '0C002927', '18FF5027',
'18F00503', '18FF5127', '18FEED11', '18FEE617', '1CFFAA27', '18EC0027', '18EB0027']
all_ids_length = {'0CF00400': 50, '0CF00300': 20, '18FEF100': 10, '1CFF6F00': 10, '18ECFF00': 1, '18FF8800': 1,
'18FF8400': 2,
'18FEE500': 4, '18F00029': 10, '18FEF200': 10, '18FF7F00': 10, '1CFF7100': 10, '18EBFF00': 5,
'18FF8200': 10,
'18FF8600': 2, '18FEDC00': 4, '1CFF7700': 10, '18FF8900': 1, '18FEDF00': 4, '18FEE900': 3,
'18FF8700': 2,
'18FEE700': 3, '1CFEB300': 3, '18FEC100': 1, '18FEEE00': 1, '18ECFF29': 1, '18EBFF29': 3,
'0C000027': 100,
'0C000F27': 20, '18FEF111': 10, '0CF00203': 99, '0CF00327': 100, '18FF8327': 100, '0C002927': 20,
'18FF5027': 10, '18F00503': 10, '18FF5127': 1, '18FEED11': 10, '18FEE617': 1, '1CFFAA27': 10,
'18EC0027': 0, '18EB0027': 0}
arb_frequency = list(all_ids_length.values())
arb_id_indexs = [0, 1, 2, 3, 8, 9, 10, 11, 13, 16, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 39]
batch_size = 1 # we need to change the batch size to 1 inorder to make predictions
def get_id_prediction(arb_index):
file = "temp_file.txt" # read frames collected by frame fetcher which collects frames from a file in the datasets
Arb_id = temp_frame_extractor.prepare_dataset(file, arbitration_id=all_ids[
arb_index]) # returns two consecutive packets of the same arbitration IDs
bit_0, bit_1, bit_2, bit_3, bit_4, bit_5, bit_6, bit_7, bit_8, bit_9, bit_10, bit_11, bit_12, bit_13, bit_14, bit_15, bit_16, bit_17, bit_18, \
bit_19, bit_20, bit_21, bit_22, bit_23, bit_24, bit_25, bit_26, bit_27, bit_28, bit_29, bit_30, bit_31, bit_32, bit_33, bit_34, bit_35, bit_36, \
bit_37, bit_38, bit_39, bit_40, bit_41, bit_42, bit_43, bit_44, bit_45, bit_46, bit_47, bit_48, bit_49, bit_50, bit_51, bit_52, bit_53, bit_54, \
bit_55, bit_56, bit_57, bit_58, bit_59, bit_60, bit_61, bit_62, bit_63 = bits_extractor.extract_all_bits(Arb_id)
arb_id = predictor.ready_for_testing(bit_0, bit_1, bit_2, bit_3, bit_4, bit_5, bit_6, bit_7,
bit_8, bit_9, bit_10, bit_11, bit_12, bit_13, bit_14,
bit_15, bit_16, bit_17, bit_18, bit_19, bit_20, bit_21,
bit_22, bit_23, bit_24, bit_25, bit_26, bit_27, bit_28,
bit_29, bit_30, bit_31, bit_32, bit_33, bit_34, bit_35,
bit_36, bit_37, bit_38, bit_39, bit_40, bit_41, bit_42,
bit_43, bit_44, bit_45, bit_46, bit_47, bit_48, bit_49,
bit_50, bit_51, bit_52, bit_53, bit_54, bit_55, bit_56,
bit_57, bit_58, bit_59, bit_60, bit_61, bit_62, bit_63,
batch_size=batch_size,
model_dir='../trained_models/' + str(
arb_index) + '/training_checkpoints') # this folder is not available unless all arbitration IDs are trained first
return arb_id[0]
duration=1 # detection duration
attack_type = 'benign_data'
attack_freq = 0
breaking_duration=1
file = open('../datasets/prepared_attacks/' + attack_type + '_' + str(attack_freq) + '.txt', "r")
for line in file:
initial_time = float(line[1:18])
break
file.close()
all_packets = frames_fetcher.read_file_tolist(attack_type, attack_freq)
counter=0 # a counter for breaking out of the while loop
results_file=open('../new_results/'+attack_type+'_'+str(attack_freq)+'.txt','w')
signal_values=[] # collection of the results
while True:
counter=counter+1
next_initial_time = frames_fetcher.fetch(initial_time, duration=1, data_lines=all_packets)
initial_time = next_initial_time + 5.0 # the 5.0 is so as for the system to skip every 5 seconds for faster testing
with concurrent.futures.ProcessPoolExecutor() as executor:
results = executor.map(get_id_prediction, arb_id_indexs)
anomaly_signal = 0
for freq, res in zip(arb_frequency, results):
anomaly_signal = anomaly_signal + freq * res
anomaly_signal = anomaly_signal / sum(arb_frequency)
signal_values.append(anomaly_signal)
if counter >= breaking_duration:
break
results_file.write(str(signal_values))
results_file.close()
for line in file:
initial_time = float(line[1:18])
break
file.close()
all_packets = frames_fetcher.read_file_tolist(attack_type, attack_freq)
counter = 0 # a counter for breaking out of the while loop
results_file = open(
'../new_results/' + attack_type + '_' + str(attack_freq) + '.txt', 'w')
signal_values = [] # collection of the results
graph_timing = []
while True:
start = time.time()
counter = counter + 1
next_initial_time = frames_fetcher.fetch(initial_time,
attack_type,
attack_freq,
duration=1,
data_lines=all_packets)
initial_time = next_initial_time + 5.0 # the 5.0 is so as for the system to skip every 5 seconds for faster testing
with concurrent.futures.ProcessPoolExecutor() as executor:
results = executor.map(get_id_prediction, arb_id_indexs)
anomaly_signal = 0
for freq, res in zip(arb_frequency, results):
anomaly_signal = anomaly_signal + freq * res
anomaly_signal = anomaly_signal / sum(arb_frequency)
signal_values.append(anomaly_signal)
print(anomaly_signal)
graph_timing.append(time.time() - start)
print(graph_timing)
