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main.py
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import sys
from fingerprint import Fingerprint
from algo import replay_scenario, analyse_scenario_result, ml_based
from algo import simple_eckersley, rule_based, split_data, train_ml, optimize_lambda
from utils import get_consistent_ids, get_fingerprints_experiments
from algo import benchmark_parallel_f_ml, benchmark_parallel_f_rules, parallel_pipe_task_ml_f, parallel_pipe_task_rules_f
import MySQLdb as mdb
CONSISTENT_IDS = "getids"
REPLAY_ECKERSLEY = "replayeck"
AUTOMATE_REPLAYS = "auto"
RULE_BASED = "rules"
ML_BASED = "ml"
AUTOMATE_ML = "automl"
ALGO_NAME_TO_FUNCTION = {
"eckersley": simple_eckersley,
"rulebased": rule_based,
}
def main(argv):
con = mdb.connect('localhost', 'root', 'bdd', 'canvas_fp_project')
cur = con.cursor(mdb.cursors.DictCursor)
if argv[0] == CONSISTENT_IDS:
print("Fetching consistent user ids.")
user_id_consistent = get_consistent_ids(cur)
with open("./data/consistent_extension_ids.csv", "w") as f:
f.write("user_id\n")
for user_id in user_id_consistent:
f.write(user_id+"\n")
elif argv[0] == AUTOMATE_REPLAYS:
exp_name = argv[1]
algo_matching_name = argv[2]
nb_min_fingerprints = int(argv[3])
exp_name += "-%s-%d" % (algo_matching_name, nb_min_fingerprints)
attributes = Fingerprint.INFO_ATTRIBUTES + Fingerprint.HTTP_ATTRIBUTES + \
Fingerprint.JAVASCRIPT_ATTRIBUTES + Fingerprint.FLASH_ATTRIBUTES
algo_matching = ALGO_NAME_TO_FUNCTION[algo_matching_name]
print("Begin automation of scenarios")
print("Start fetching fingerprints...")
fingerprint_dataset = get_fingerprints_experiments(cur, nb_min_fingerprints, attributes)
train_data, test_data = split_data(0.40, fingerprint_dataset)
print("Fetched %d fingerprints." % len(fingerprint_dataset))
# we iterate on different values of visit_frequency
visit_frequencies = [1, 2, 3, 4, 5, 6, 7, 8, 10, 15, 20]
for visit_frequency in visit_frequencies:
result_scenario = replay_scenario(test_data, visit_frequency,
algo_matching,
filename="./results/"+exp_name+"_"+str(visit_frequency)+"scenario_replay_result.csv")
analyse_scenario_result(result_scenario, test_data,
fileres1="./results/"+exp_name+"_"+str(visit_frequency)+"-res1.csv",
fileres2="./results/"+exp_name+"_"+str(visit_frequency)+"-res2.csv",
)
elif argv[0] == AUTOMATE_ML:
print("Start automating ml based scenario")
exp_name = argv[1]
algo_matching_name = "hybridalgo"
nb_min_fingerprints = int(argv[2])
exp_name += "-%s-%d" % (algo_matching_name, nb_min_fingerprints)
attributes = Fingerprint.INFO_ATTRIBUTES + Fingerprint.HTTP_ATTRIBUTES + \
Fingerprint.JAVASCRIPT_ATTRIBUTES + Fingerprint.FLASH_ATTRIBUTES
print("Begin automation of scenarios")
print("Start fetching fingerprints...")
fingerprint_dataset = get_fingerprints_experiments(cur, nb_min_fingerprints, attributes)
print("Fetched %d fingerprints." % len(fingerprint_dataset))
train_data, test_data = split_data(0.40, fingerprint_dataset)
model = train_ml(fingerprint_dataset, train_data, load=True)
# we iterate on different values of visit_frequency
visit_frequencies = [1, 2, 3, 4, 5, 6, 7, 8, 10, 15, 20]
for visit_frequency in visit_frequencies:
result_scenario = replay_scenario(test_data, visit_frequency,
ml_based,
filename="./results/"+exp_name+"_"+str(visit_frequency)+"scenario_replay_result.csv",
model=model, lambda_threshold=0.994)
analyse_scenario_result(result_scenario, test_data,
fileres1="./results/"+exp_name+"_"+str(visit_frequency)+"-res1.csv",
fileres2="./results/"+exp_name+"_"+str(visit_frequency)+"-res2.csv",
)
elif argv[0] == "lambda":
attributes = Fingerprint.INFO_ATTRIBUTES + Fingerprint.HTTP_ATTRIBUTES + \
Fingerprint.JAVASCRIPT_ATTRIBUTES + Fingerprint.FLASH_ATTRIBUTES
nb_min_fingerprints = 6
print("Start fetching fingerprints...")
fingerprint_dataset = get_fingerprints_experiments(cur, nb_min_fingerprints, attributes)
print("Fetched %d fingerprints." % len(fingerprint_dataset))
train_data, test_data = split_data(0.4, fingerprint_dataset)
optimize_lambda(fingerprint_dataset, train_data, test_data)
elif argv[0] == "automlbench":
prefix_files = argv[1]
nb_cores = int(argv[2])
nb_processes = [1, 2, 4, 8, 16, 24, 32]
nb_fingerprints = [500000, 1000000, 2000000]
# nb_fingerprints = [500000, 1000000, 2000000]
fn = parallel_pipe_task_ml_f
with open("./benchres/%s.csv" % prefix_files, "w")as f:
f.write("%s,%s,%s,%s,%s,%s,%s,%s,%s\n" %
("nb_fingerprints",
"nb_cores",
"nb_processes",
"avg",
"max",
"min",
"median",
"q1",
"q3")
)
for nb_fingerprint in nb_fingerprints:
for nb_process in nb_processes:
mean, min, max, p25, p50, p75 = benchmark_parallel_f_ml(fn, cur, nb_fingerprint, nb_process)
f.write("%d,%d,%d,%f,%f,%f,%f,%f,%f\n" % (
nb_fingerprint,
nb_cores,
nb_process,
mean,
max,
min,
p50,
p25,
p75
))
elif argv[0] == "autorulesbench":
prefix_files = argv[1]
nb_cores = int(argv[2])
nb_processes = [1, 2, 4, 8, 16, 24, 32]
nb_fingerprints = [500000, 1000000, 2000000]
fn = parallel_pipe_task_rules_f
with open("./benchres/%s.csv" % prefix_files, "w")as f:
f.write("%s,%s,%s,%s,%s,%s,%s,%s,%s\n" %
("nb_fingerprints",
"nb_cores",
"nb_processes",
"avg",
"max",
"min",
"median",
"q1",
"q3")
)
for nb_fingerprint in nb_fingerprints:
for nb_process in nb_processes:
mean, min, max, p25, p50, p75 = benchmark_parallel_f_rules(fn, cur, nb_fingerprint, nb_process)
f.write("%d,%d,%d,%f,%f,%f,%f,%f,%f\n" % (
nb_fingerprint,
nb_cores,
nb_process,
mean,
max,
min,
p50,
p25,
p75
))
if __name__ == "__main__":
main(sys.argv[1:])