{ "info": { "author": "Zied Aouini", "author_email": "aouinizied@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Intended Audience :: Information Technology", "Intended Audience :: Science/Research", "Intended Audience :: System Administrators", "Intended Audience :: Telecommunications Industry", "License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)", "Programming Language :: Python :: 3 :: Only", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Topic :: Internet :: Log Analysis", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Security", "Topic :: System :: Networking :: Monitoring" ], "description": "![NFStream Logo](https://raw.githubusercontent.com/nfstream/nfstream/master/assets/nfstream_header_logo.png?raw=true)\n\n--------------------------------------------------------------------------------\n[**NFStream**][repo] is a multiplatform Python framework providing fast, flexible, and expressive data structures designed to make \nworking with **online** or **offline** network data both easy and intuitive. It aims to be the fundamental high-level \nbuilding block for doing practical, **real world** network data analysis in Python. Additionally, it has the broader \ngoal of becoming **a common network data analytics framework for researchers** providing data reproducibility \nacross experiments.\n\n\n\n \n \n\n\n \n \n\n\n \n \n\n\n \n \n\n\n\n \n \n\n\n \n \n\n\n \n \n\n\n \n \n\n\n \n \n\n
Live Notebook\n \n \"live\n \n
Project Website\n \n \"website\"\n \n
Discussion Channel\n \n \"Gitter\"\n \n
Latest Release\n \n \"latest\n \n
Supported Versions\n \n \"python3\"\n \n \n \"pypy3\"\n \n
Project License\n \n \"License\"\n \n
Continuous Integration\n \n \"Linux\n \n \n \"MacOS\n \n \n \"Windows\n \n \n \"ARM64\n \n \n \"ARM32\n \n
Code Quality\n \n \"Quality\"\n \n \n \"Quality\"\n \n
Code Coverage\n \n \"Coverage\"\n \n
\n\n## Table of Contents\n\n * [**Main Features**](#main-features)\n * [**How to get it?**](#how-to-get-it)\n * [**How to use it?**](#how-to-use-it)\n * [**Encrypted application identification and metadata extraction**](#encrypted-application-identification-and-metadata-extraction)\n * [**System visibility**](#system-visibility)\n * [**Post-mortem statistical flow features extraction**](#post-mortem-statistical-flow-features-extraction)\n * [**Early statistical flow features extraction**](#early-statistical-flow-features-extraction)\n * [**Pandas export interface**](#pandas-export-interface)\n * [**CSV export interface**](#csv-export-interface)\n * [**Extending NFStream**](#extending-nfstream)\n * [**Machine Learning models training and deployment**](#machine-learning-models-training-and-deployment)\n * [**Training the model**](#training-the-model)\n * [**ML powered streamer on live traffic**](#ml-powered-streamer-on-live-traffic)\n * [**Building from sources**](#building-from-sources--)\n * [**Contributing**](#contributing)\n * [**Ethics**](#ethics)\n * [**Credits**](#credits)\n * [**Citation**](#citation)\n * [**Authors**](#authors)\n * [**Supporting organizations**](#supporting-organizations)\n * [**Publications that use NFStream**](#publications-that-use-nfstream)\n * [**License**](#license)\n\n## Main Features\n\n* **Performance:** NFStream is designed to be fast: AF_PACKETV3/FANOUT on Linux, parallel processing, native C \n(using [**CFFI**][cffi]) for critical computation and [**PyPy**][pypy] support.\n* **Encrypted layer-7 visibility:** NFStream deep packet inspection is based on [**nDPI**][ndpi]. \nIt allows NFStream to perform [**reliable**][reliable] encrypted applications identification and metadata \nfingerprinting (e.g. TLS, SSH, DHCP, HTTP).\n* **System visibility:** NFStream probes the monitored system's kernel to obtain information on open Internet sockets \nand collects guaranteed ground-truth (process name, PID, etc.) at the application level.\n* **Statistical features extraction:** NFStream provides state of the art of flow-based statistical feature extraction. \nIt includes both post-mortem statistical features (e.g. min, mean, stddev and max of packet size and inter arrival time) \nand early flow features (e.g. sequence of first n packets sizes, inter arrival times and\ndirections).\n* **Flexibility:** NFStream is easily extensible using [**NFPlugins**][nfplugin]. It allows to create a new \nfeature within a few lines of Python.\n* **Machine Learning oriented:** NFStream aims to make Machine Learning Approaches for network traffic management \nreproducible and deployable. By using NFStream as a common framework, researchers ensure that models are trained using \nthe same feature computation logic and thus, a fair comparison is possible. Moreover, trained models can be deployed \nand evaluated on live network using [**NFPlugins**][nfplugin]. \n\n## How to get it?\n\nBinary installers for the latest released version are available on Pypi.\n\n```bash\npip install nfstream\n```\n\n> **Windows Notes**: NFStream does not include capture drivers on Windows. It is required to install \n> [Npcap drivers][npcap] before installing NFStream.\n> If Wireshark is already installed on Windows, then Npcap drivers are already installed.\n\n## How to use it?\n\n### Encrypted application identification and metadata extraction\n\nDealing with a big pcap file and just want to aggregate into labeled network flows? **NFStream** make this path easier \nin few lines:\n\n```python\nfrom nfstream import NFStreamer\n# We display all streamer parameters with their default values.\n# See documentation for detailed information about each parameter.\n# https://www.nfstream.org/docs/api#nfstreamer\nmy_streamer = NFStreamer(source=\"facebook.pcap\", # or network interface\n decode_tunnels=True,\n bpf_filter=None,\n promiscuous_mode=True,\n snapshot_length=1536,\n idle_timeout=120,\n active_timeout=1800,\n accounting_mode=0,\n udps=None,\n n_dissections=20,\n statistical_analysis=False,\n splt_analysis=0,\n n_meters=0,\n performance_report=0,\n system_visibility_mode=0,\n system_visibility_poll_ms=100,\n system_visibility_extension_port=28314)\n \nfor flow in my_streamer:\n print(flow) # print it.\n```\n\n```python\n# See documentation for each feature detailed description.\n# https://www.nfstream.org/docs/api#nflow\nNFlow(id=0,\n expiration_id=0,\n src_ip='192.168.43.18',\n src_mac='30:52:cb:6c:9c:1b',\n src_oui='30:52:cb',\n src_port=52066,\n dst_ip='66.220.156.68',\n dst_mac='98:0c:82:d3:3c:7c',\n dst_oui='98:0c:82',\n dst_port=443,\n protocol=6,\n ip_version=4,\n vlan_id=0,\n tunnel_id=0,\n bidirectional_first_seen_ms=1472393122365,\n bidirectional_last_seen_ms=1472393123665,\n bidirectional_duration_ms=1300,\n bidirectional_packets=19,\n bidirectional_bytes=5745,\n src2dst_first_seen_ms=1472393122365,\n src2dst_last_seen_ms=1472393123408,\n src2dst_duration_ms=1043,\n src2dst_packets=9,\n src2dst_bytes=1345,\n dst2src_first_seen_ms=1472393122668,\n dst2src_last_seen_ms=1472393123665,\n dst2src_duration_ms=997,\n dst2src_packets=10,\n dst2src_bytes=4400,\n application_name='TLS.Facebook',\n application_category_name='SocialNetwork',\n application_is_guessed=0,\n application_confidence=4,\n requested_server_name='facebook.com',\n client_fingerprint='bfcc1a3891601edb4f137ab7ab25b840',\n server_fingerprint='2d1eb5817ece335c24904f516ad5da12',\n user_agent='',\n content_type='')\n ```\n\n### System visibility\n\nNFStream probes the monitored system's kernel to obtain information on open Internet sockets and collects guaranteed \nground-truth (process name, PID, etc.) at the application level.\n\n```python\nfrom nfstream import NFStreamer\nmy_streamer = NFStreamer(source=\"Intel(R) Wi-Fi 6 AX200 160MHz\", # Live capture mode. \n # Disable L7 dissection for readability purpose only.\n n_dissections=0,\n system_visibility_poll_ms=100,\n system_visibility_mode=1)\n \nfor flow in my_streamer:\n print(flow) # print it.\n```\n\n```python\n# See documentation for each feature detailed description.\n# https://www.nfstream.org/docs/api#nflow\nNFlow(id=0,\n expiration_id=0,\n src_ip='192.168.43.18',\n src_mac='30:52:cb:6c:9c:1b',\n src_oui='30:52:cb',\n src_port=59339,\n dst_ip='184.73.244.37',\n dst_mac='98:0c:82:d3:3c:7c',\n dst_oui='98:0c:82',\n dst_port=443,\n protocol=6,\n ip_version=4,\n vlan_id=0,\n tunnel_id=0,\n bidirectional_first_seen_ms=1638966705265,\n bidirectional_last_seen_ms=1638966706999,\n bidirectional_duration_ms=1734,\n bidirectional_packets=98,\n bidirectional_bytes=424464,\n src2dst_first_seen_ms=1638966705265,\n src2dst_last_seen_ms=1638966706999,\n src2dst_duration_ms=1734,\n src2dst_packets=22,\n src2dst_bytes=2478,\n dst2src_first_seen_ms=1638966705345,\n dst2src_last_seen_ms=1638966706999,\n dst2src_duration_ms=1654,\n dst2src_packets=76,\n dst2src_bytes=421986,\n # The process that generated this reported flow. \n system_process_pid=14596,\n system_process_name='FortniteClient-Win64-Shipping.exe')\n ```\n\n### Post-mortem statistical flow features extraction\n\nNFStream performs 48 post mortem flow statistical features extraction which include detailed TCP flags analysis, \nminimum, mean, maximum and standard deviation of both packet size and interarrival time in each direction. \n\n```python\nfrom nfstream import NFStreamer\nmy_streamer = NFStreamer(source=\"facebook.pcap\",\n # Disable L7 dissection for readability purpose.\n n_dissections=0, \n statistical_analysis=True)\nfor flow in my_streamer:\n print(flow)\n```\n\n```python\n# See documentation for each feature detailed description.\n# https://www.nfstream.org/docs/api#nflow\nNFlow(id=0,\n expiration_id=0,\n src_ip='192.168.43.18',\n src_mac='30:52:cb:6c:9c:1b',\n src_oui='30:52:cb',\n src_port=52066,\n dst_ip='66.220.156.68',\n dst_mac='98:0c:82:d3:3c:7c',\n dst_oui='98:0c:82',\n dst_port=443,\n protocol=6,\n ip_version=4,\n vlan_id=0,\n tunnel_id=0,\n bidirectional_first_seen_ms=1472393122365,\n bidirectional_last_seen_ms=1472393123665,\n bidirectional_duration_ms=1300,\n bidirectional_packets=19,\n bidirectional_bytes=5745,\n src2dst_first_seen_ms=1472393122365,\n src2dst_last_seen_ms=1472393123408,\n src2dst_duration_ms=1043,\n src2dst_packets=9,\n src2dst_bytes=1345,\n dst2src_first_seen_ms=1472393122668,\n dst2src_last_seen_ms=1472393123665,\n dst2src_duration_ms=997,\n dst2src_packets=10,\n dst2src_bytes=4400,\n bidirectional_min_ps=66,\n bidirectional_mean_ps=302.36842105263156,\n bidirectional_stddev_ps=425.53315715259754,\n bidirectional_max_ps=1454,\n src2dst_min_ps=66,\n src2dst_mean_ps=149.44444444444446,\n src2dst_stddev_ps=132.20354676701294,\n src2dst_max_ps=449,\n dst2src_min_ps=66,\n dst2src_mean_ps=440.0,\n dst2src_stddev_ps=549.7164925870628,\n dst2src_max_ps=1454,\n bidirectional_min_piat_ms=0,\n bidirectional_mean_piat_ms=72.22222222222223,\n bidirectional_stddev_piat_ms=137.34994188549086,\n bidirectional_max_piat_ms=398,\n src2dst_min_piat_ms=0,\n src2dst_mean_piat_ms=130.375,\n src2dst_stddev_piat_ms=179.72036811192467,\n src2dst_max_piat_ms=415,\n dst2src_min_piat_ms=0,\n dst2src_mean_piat_ms=110.77777777777777,\n dst2src_stddev_piat_ms=169.51458475436397,\n dst2src_max_piat_ms=409,\n bidirectional_syn_packets=2,\n bidirectional_cwr_packets=0,\n bidirectional_ece_packets=0,\n bidirectional_urg_packets=0,\n bidirectional_ack_packets=18,\n bidirectional_psh_packets=9,\n bidirectional_rst_packets=0,\n bidirectional_fin_packets=0,\n src2dst_syn_packets=1,\n src2dst_cwr_packets=0,\n src2dst_ece_packets=0,\n src2dst_urg_packets=0,\n src2dst_ack_packets=8,\n src2dst_psh_packets=4,\n src2dst_rst_packets=0,\n src2dst_fin_packets=0,\n dst2src_syn_packets=1,\n dst2src_cwr_packets=0,\n dst2src_ece_packets=0,\n dst2src_urg_packets=0,\n dst2src_ack_packets=10,\n dst2src_psh_packets=5,\n dst2src_rst_packets=0,\n dst2src_fin_packets=0)\n```\n\n### Early statistical flow features extraction\nNFStream performs early (up to 255 packets) flow statistical features extraction (also referred as SPLT analysis in the \nliterature). It is summarized as a sequence a these packets directions, sizes and interarrival times.\n\n```python\nfrom nfstream import NFStreamer\nmy_streamer = NFStreamer(source=\"facebook.pcap\",\n # We disable l7 dissection for readability purpose.\n n_dissections=0,\n splt_analysis=10)\nfor flow in my_streamer:\n print(flow)\n```\n\n```python\n# See documentation for each feature detailed description.\n# https://www.nfstream.org/docs/api#nflow\nNFlow(id=0,\n expiration_id=0,\n src_ip='192.168.43.18',\n src_mac='30:52:cb:6c:9c:1b',\n src_oui='30:52:cb',\n src_port=52066,\n dst_ip='66.220.156.68',\n dst_mac='98:0c:82:d3:3c:7c',\n dst_oui='98:0c:82',\n dst_port=443,\n protocol=6,\n ip_version=4,\n vlan_id=0,\n tunnel_id=0,\n bidirectional_first_seen_ms=1472393122365,\n bidirectional_last_seen_ms=1472393123665,\n bidirectional_duration_ms=1300,\n bidirectional_packets=19,\n bidirectional_bytes=5745,\n src2dst_first_seen_ms=1472393122365,\n src2dst_last_seen_ms=1472393123408,\n src2dst_duration_ms=1043,\n src2dst_packets=9,\n src2dst_bytes=1345,\n dst2src_first_seen_ms=1472393122668,\n dst2src_last_seen_ms=1472393123665,\n dst2src_duration_ms=997,\n dst2src_packets=10,\n dst2src_bytes=4400,\n # The sequence of 10 first packet direction, size and inter arrival time.\n splt_direction=[0, 1, 0, 0, 1, 1, 0, 1, 0, 1],\n splt_ps=[74, 74, 66, 262, 66, 1454, 66, 1454, 66, 463],\n splt_piat_ms=[0, 303, 0, 0, 313, 0, 0, 0, 0, 1])\n```\n\n### Pandas export interface\n\nNFStream natively supports Pandas as export interface.\n\n```python\n# See documentation for more details.\n# https://www.nfstream.org/docs/api#pandas-dataframe-conversion\nfrom nfstream import NFStreamer\nmy_dataframe = NFStreamer(source='teams.pcap').to_pandas()[[\"src_ip\",\n \"src_port\",\n \"dst_ip\", \n \"dst_port\", \n \"protocol\",\n \"bidirectional_packets\",\n \"bidirectional_bytes\",\n \"application_name\"]]\nmy_dataframe.head(5)\n```\n\n![Pandas](https://raw.githubusercontent.com/nfstream/nfstream/master/assets/pandas_df.png?raw=true)\n\n\n### CSV export interface\n\nNFStream natively supports CSV file format as export interface.\n\n```python\n# See documentation for more details.\n# https://www.nfstream.org/docs/api#csv-file-conversion\nflows_count = NFStreamer(source='facebook.pcap').to_csv(path=None,\n columns_to_anonymize=(),\n flows_per_file=0,\n rotate_files=0)\n```\n\n### Extending NFStream\n\nDidn't find a specific flow feature? add a plugin to **NFStream** in few lines:\n\n```python\nfrom nfstream import NFPlugin\n \nclass MyCustomFeature(NFPlugin):\n def on_init(self, packet, flow):\n # flow creation with the first packet\n if packet.raw_size == self.custom_size:\n flow.udps.packet_with_custom_size = 1\n else:\n flow.udps.packet_with_custom_size = 0\n\t\n def on_update(self, packet, flow):\n # flow update with each packet belonging to the flow \n if packet.raw_size == self.custom_size:\n flow.udps.packet_with_custom_size += 1\n\n\nextended_streamer = NFStreamer(source='facebook.pcap', \n udps=MyCustomFeature(custom_size=555))\n\nfor flow in extended_streamer:\n # see your dynamically created metric in generated flows\n print(flow.udps.packet_with_custom_size) \n```\n\n### Machine Learning models training and deployment\n\nIn the following example, we demonstrate a simplistic machine learning approach training and deployment.\nWe suppose that we want to run a classification of Social Network category flows based on bidirectional_packets and \nbidirectional_bytes as features. For the sake of brevity, we decide to predict only at flow expiration stage.\n\n#### Training the model\n\n```python\nfrom nfstream import NFPlugin, NFStreamer\nimport numpy\nfrom sklearn.ensemble import RandomForestClassifier\n\ndf = NFStreamer(source=\"training_traffic.pcap\").to_pandas()\nX = df[[\"bidirectional_packets\", \"bidirectional_bytes\"]]\ny = df[\"application_category_name\"].apply(lambda x: 1 if 'SocialNetwork' in x else 0)\nmodel = RandomForestClassifier()\nmodel.fit(X, y)\n```\n\n#### ML powered streamer on live traffic\n\n```python\nclass ModelPrediction(NFPlugin):\n def on_init(self, packet, flow):\n flow.udps.model_prediction = 0\n def on_expire(self, flow):\n # You can do the same in on_update entrypoint and force expiration with custom id. \n to_predict = numpy.array([flow.bidirectional_packets,\n flow.bidirectional_bytes]).reshape((1,-1))\n flow.udps.model_prediction = self.my_model.predict(to_predict)\n\nml_streamer = NFStreamer(source=\"eth0\", udps=ModelPrediction(my_model=model))\nfor flow in ml_streamer:\n print(flow.udps.model_prediction)\n```\n\nMore NFPlugin examples and details are provided on the official [**documentation**][documentation]. You can also test \nNFStream without installation using our [**live demo notebook**][demo].\n\n## Building from sources ![l] ![m] ![w] \n\nIf you want to build **NFStream** from sources. Please read the [**installation guide**][install].\n\n## Contributing\n\nPlease read [**Contributing**][contribute] for details on our code of conduct, and the process for submitting pull\nrequests to us.\n\n## Ethics\n\n**NFStream** is intended for network data research and forensics.\nResearchers and network data scientists can use these framework to build reliable datasets, train and evaluate\nnetwork applied machine learning models.\nAs with any packet monitoring tool, **NFStream** could potentially be misused.\n**Do not run it on any network of which you are not the owner or the administrator**.\n\n## Credits\n\n### Citation\n\n[**NFStream paper**][doi] is published in [**Computer Networks (COMNET)**][comnet]. If you use NFStream in a scientific \npublication, we would appreciate citations to the following paper:\n\n``` latex\n@article{AOUINI2022108719,\n title = {NFStream: A flexible network data analysis framework},\n author = {Aouini, Zied and Pekar, Adrian},\n doi = {10.1016/j.comnet.2021.108719},\n issn = {1389-1286},\n journal = {Computer Networks},\n pages = {108719},\n year = {2022},\n publisher = {Elsevier},\n volume = {204},\n url = {https://www.sciencedirect.com/science/article/pii/S1389128621005739}\n}\n```\n\n### Authors\n\nThe following people contributed to NFStream:\n* [**Zied Aouini**](mailto:aouinizied@gmail.com): Creator and main developer.\n* [**Adrian Pekar**](mailto:adrian.pekar@gmail.com): Testing, datasets generation and storage.\n* [**Romain Picard**](mailto:romain.picard@oakbits.com): Several Plugins implementation.\n* [**Radion Bikmukhamedov**](mailto:radion.bikmukhamedov@pm.me): Initial work on SPLT analysis NFPlugin.\n\n### Supporting organizations\n\nThe following organizations are supporting NFStream:\n* [**SoftAtHome**](https://www.softathome.com/): Main supporter of NFStream development.\n* [**Technical University of Ko\u0161ice**](https://www.tuke.sk/): Hardware and infrastructure for datasets generation and \nstorage.\n* [**ntop**](https://www.ntop.org/): Technical support of [**nDPI**][ndpi] integration.\n* [**The Nmap Project**](https://nmap.org): Technical support of [**Npcap**][npcap] integration (Windows CI).\n\n[![sah]](https://www.softathome.com/) [![tuke]](https://www.tuke.sk/) [![ntop]](https://www.ntop.org/) [![nmap]](https://nmap.org/)\n\n## Publications that use NFStream\n\n* [**A Hierarchical Architecture and Probabilistic Strategy for Collaborative Intrusion Detectionn**](https://ieeexplore.ieee.org/abstract/document/9705027)\n* [**Robust Variational Autoencoders and Normalizing Flows for Unsupervised Network Anomaly Detection**](https://hal.archives-ouvertes.fr/hal-03542451/document)\n* [**RADON: Robust Autoencoder for Unsupervised Anomaly Detection**](https://ieeexplore.ieee.org/document/9699174)\n* [**A Generic Machine Learning Approach for IoT Device Identification**](https://ieeexplore.ieee.org/document/9702983)\n* [**Ranking Network Devices for Alarm Prioritisation: Intrusion Detection Case Study**](https://ieeexplore.ieee.org/abstract/document/9559086)\n* [**Network Flows-Based Malware Detection Using A Combined Approach of Crawling And Deep Learning**](https://ieeexplore.ieee.org/document/9500920)\n* [**Network Intrusion Detection Based on Distributed Trustworthy Artificial Intelligence**](https://repository.dl.itc.u-tokyo.ac.jp/record/2002253/files/48196454.pdf)\n* [**Generative Transformer Framework For Network Traffic Generation And Classification**](https://cyberleninka.ru/article/n/generative-transformer-framework-for-network-traffic-generation-and-classification)\n* [**Multi-Class Network Traffic Generators and Classifiers Based on Neural Networks**](https://ieeexplore.ieee.org/document/9416067)\n* [**Using Embedded Feature Selection and CNN for Classification on CCD-INID-V1 A New IoT Dataset**](https://www.mdpi.com/1424-8220/21/14/4834)\n* [**An Approach Based on Knowledge-Defined Networking for Identifying Video Streaming Flows in 5G Networks**](https://latamt.ieeer9.org/index.php/transactions/article/view/5083/1116)\n* [**Knowledge Discovery: Can It Shed New Light on Threshold Definition for Heavy\u2011Hitter Detection?**](https://link.springer.com/content/pdf/10.1007/s10922-021-09593-w.pdf)\n* [**Collecting and analyzing Tor exit node traffic**](https://www.diva-portal.org/smash/get/diva2:1575255/FULLTEXT01.pdf)\n\n## License\n\nThis project is licensed under the LGPLv3 License - see the [**License**][license] file for details\n\n[license]: https://github.com/nfstream/nfstream/blob/master/LICENSE\n[contribute]: https://nfstream.org/docs/community\n[contributors]: https://github.com/nfstream/nfstream/graphs/contributors\n[documentation]: https://nfstream.org/\n[ndpi]: https://github.com/ntop/nDPI\n[npcap]: https://npcap.org\n[nfplugin]: https://nfstream.org/docs/api#nfplugin\n[reliable]: http://people.ac.upc.edu/pbarlet/papers/ground-truth.pam2014.pdf\n[repo]: https://nfstream.org/\n[demo]: https://mybinder.org/v2/gh/nfstream/nfstream/master?filepath=demo_notebook.ipynb\n[pypy]: https://www.pypy.org/\n[cffi]: 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