Metadata-Version: 2.4
Name: logdag
Version: 0.2.0
Summary: A tool to generate causal DAGs from syslog time-series.
Home-page: https://github.com/cpflat/logdag/
Author: Satoru Kobayashi
Author-email: sat@3at.work
License: The 3-Clause BSD License
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries :: Python Modules
License-File: LICENSE
Requires-Dist: amulog>=0.4.0
Requires-Dist: pcalg>=0.1.9
Requires-Dist: gsq>=0.1.6
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: python-dateutil
Requires-Dist: networkx>=2.1
Requires-Dist: lingam
Requires-Dist: statsmodels
Provides-Extra: influx
Requires-Dist: influxdb; extra == "influx"
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: home-page
Dynamic: license
Dynamic: license-file
Dynamic: provides-extra
Dynamic: requires-dist
Dynamic: summary

######
logdag
######

Overview
========

This package generates causal DAGs among time-series events in syslog data.
This package works on python3.
The input log data is loaded with AMULOG (https://github.com/cpflat/amulog).
The output DAG is recorded in the format of NetworkX DiGraph.

This project was partially forked from repository LogCausalAnaysis.
(https://github.com/cpflat/LogCausalAnalysis)


Usage
======

All features are available from command line. First you should try following help command :code:`python -m logdag -h`.

short usage:

1. Generate amulog database (and its config) as the input log time-series source
2. Prepare logdag config file by referring :code:`logdag/data/config.conf.default`
3. Generate time-series db by :code:`logdag.source` features
4. Generate DAGs by subcommand :code:`makedag`
5. See results by commands such as :code:`show-subgraphs`

See tutorial directory for more information.

Reference
=========

This project is evaluated in some papers `CNSM2019 <https://doi.org/10.23919/CNSM46954.2019.9012718>`_ and `TNSM2018 <https://doi.org/10.1109/TNSM.2017.2778096>`_.
If you use this code, please consider citing:

::

    @inproceedings{Kobayashi_CNSM2019,
      author = {Kobayashi, Satoru and Otomo, Kazuki and Fukuda, Kensuke},
      booktitle = {Proceedings of the 15th International Conference on Network and Service Management (CNSM'20)},
      title = {Causal analysis of network logs with layered protocols and topology knowledge},
      pages = {1-9},
      year = {2019}
    }


    @article{Kobayashi_TNSM2018,
      author = {Kobayashi, Satoru and Otomo, Kazuki and Fukuda, Kensuke and Esaki, Hiroshi},
      journal = {IEEE Transactions on Network and Service Management},
      volume = {15},
      number = {1},
      pages = {53-67},
      title = {Mining causes of network events in log data with causal inference},
      year = {2018}
    }


License
=======

3-Clause BSD license

Author
======

Satoru Kobayashi

