65 lines
2.2 KiB
Python
65 lines
2.2 KiB
Python
# 输入一系列pcap和pcap对应的标签，输出一个dataframe格式的csv
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from time import time
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from configparser import ConfigParser
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from multiprocessing import cpu_count
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import pandas as pd
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import sys
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import os
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from concurrent.futures import ThreadPoolExecutor
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from threading import Lock
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import re
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lock = Lock()
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pcap_name_complier = re.compile(".*/(?P<domain>.*)_(?P<doh_uri>.*)_(?P<browser>.*)_(?P<time>.*)\.pcap")
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def multi_write(input_filename, id):
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match_result = pcap_name_complier.match(input_filename)
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if match_result:
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domain = match_result["domain"]
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doh_uri = match_result["doh_uri"]
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browser = match_result["browser"]
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else:
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domain = None
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doh_uri = None
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browser = None
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lengths, time_lags, directions = trans_pcap_to_row(input_filename)
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if len(lengths) == 0 or sum(time_lags) < 1e-7:
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return
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flow_serial = [lengths[i] * directions[i] for i in range(len(lengths))]
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time_serial = [time_lags[i] * directions[i] for i in range(len(time_lags))]
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features = extract_statistic_feature(lengths, time_lags, directions)
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lock.acquire()
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df.loc[id] = [domain, doh_uri, browser, flow_serial, time_serial, features]
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lock.release()
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def run(input_filepath):
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files = os.listdir(input_filepath)
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with ThreadPoolExecutor(max_workers=30) as thread_pool:
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id = 0
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for i in range(len(files)):
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file = files[i]
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full_file = os.path.join(input_filepath, file)
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if not full_file.endswith(".pcap"):
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continue
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# multi_write(full_file, id)
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thread_pool.submit(multi_write, full_file, id)
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id += 1
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print(id)
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if __name__ == "__main__":
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# input_filepath = sys.argv[1]
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# print(input_filepath)
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# output_filename = sys.argv[2]
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input_filepath = "E:\\doh&web\\201102\\win10\\web_traffic_after_predeal"
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output_filename = "./result/web_feature_2.csv"
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start_time = time()
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df = pd.DataFrame(columns=["domain", "doh_uri", "browser", "flow_serial", "time_serial", "features"])
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run(input_filepath)
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df.to_csv(output_filename)
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end_time = time()
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print("总耗时：", end_time - start_time)
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