mirror of
https://github.com/nfstream/nfstream.git
synced 2026-04-26 14:10:37 +00:00
49 lines
2.1 KiB
Python
49 lines
2.1 KiB
Python
"""
|
|
------------------------------------------------------------------------------------------------------------------------
|
|
generate_results.py
|
|
Copyright (C) 2019-22 - NFStream Developers
|
|
This file is part of NFStream, a Flexible Network Data Analysis Framework (https://www.nfstream.org/).
|
|
NFStream is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public
|
|
License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later
|
|
version.
|
|
NFStream is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty
|
|
of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.
|
|
You should have received a copy of the GNU Lesser General Public License along with NFStream.
|
|
If not, see <http://www.gnu.org/licenses/>.
|
|
------------------------------------------------------------------------------------------------------------------------
|
|
"""
|
|
|
|
from nfstream import NFStreamer
|
|
from tqdm import tqdm
|
|
import os
|
|
|
|
# This script is used to generate results files under tests repository.
|
|
|
|
|
|
def get_files_list(path):
|
|
files = []
|
|
for r, d, f in os.walk(path):
|
|
for file in f:
|
|
if (
|
|
".pcap" == file[-5:] or ".pcapng" == file[-7:]
|
|
): # Pick out only pcaps files
|
|
files.append(os.path.join(r, file))
|
|
files.sort()
|
|
return files
|
|
|
|
|
|
if __name__ == "__main__": # Mandatory if you are running on Windows Platform
|
|
pcap_files = get_files_list(os.path.join("tests", "pcaps"))
|
|
for pcap_file in tqdm(pcap_files):
|
|
df = NFStreamer(source=pcap_file, n_dissections=20, n_meters=1).to_pandas()[
|
|
[
|
|
"id",
|
|
"bidirectional_packets",
|
|
"bidirectional_bytes",
|
|
"application_name",
|
|
"application_category_name",
|
|
"application_is_guessed",
|
|
"application_confidence",
|
|
]
|
|
]
|
|
df.to_csv(pcap_file.replace("pcaps", "results"), index=False)
|