Need help with log-anomaly-detector?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

AICoE
161 Stars 78 Forks GNU General Public License v3.0 465 Commits 66 Opened issues

Description

Log Anomaly Detection - Machine learning to detect abnormal events logs

Services available

!
?

Need anything else?

Contributors list

====================

Log Anomaly Detector

.. image:: https://api.travis-ci.org/aicoe/log-anomaly-detector.png?branch=master :target: http://travis-ci.org/aicoe/log-anomaly-detector .. image:: https://img.shields.io/pypi/v/log-anomaly-detector.svg :target: https://pypi.python.org/pypi/log-anomaly-detector/ .. image:: https://img.shields.io/pypi/dm/log-anomaly-detector.svg :target: https://pypi.python.org/pypi/log-anomaly-detector/ .. image:: https://img.shields.io/pypi/wheel/log-anomaly-detector.svg :target: https://pypi.python.org/pypi/log-anomaly-detector/ :alt: Wheel Status .. image:: https://readthedocs.org/projects/log-anomaly-detector/badge/?version=latest :target: https://log-anomaly-detector.readthedocs.io/en/latest/

Log anomaly detector is an open source project code named "Project Scorpio". LAD is also used for short. It can connect to streaming sources and produce predictions of abnormal log lines. Internally it uses unsupervised machine learning. We incorporate a number of machine learning models to achieve this result. In addition it includes a human in the loop feedback system.

.. image:: imgs/full-app.gif

Project background

The original goal for this project was to develop an automated means of notifying users when problems occur with their applications based on the information contained in their application logs. Unfortunately logs are full of messages that contain warnings or even errors that are safe to ignore, so simple “find-keyword” methods are insufficient . In addition, the number of logs are increasing constantly and no human will, or can, monitor them all. In short, our original aim was to employ natural language processing tools for text encoding and machine learning methods for automated anomaly detection, in an effort to construct a tool that could help developers perform root cause analysis more quickly on failing applications by highlighting the logs most likely to provide insight into the problem or to generate an alert if an application starts to produce a high frequency of anomalous logs.

Components

It currently contains the following components:

.. image:: imgs/components.png

  1. LAD-Core: Contains custom code to train model and predict if a log line is an anomaly. We are currently use W2V (word 2 vec) and SOM (self organizing map) with unsupervised machine learning. We are planning to add more models.
  2. Metrics: To monitor this system in production we utilize grafana and prometheus to visualize the health of this machine learning system.
  3. Fact-Store: In addition we have a metadata registry for tracking feedback from false_positives in the machine learning system and to providing a method for ML to self correcting false predictions called the “fact-store”.

INSTALLING THE PKG

Using pip::

$ pip install log-anomaly-detector

...Or simply add it to your requirements.

.. note::

LAD requires python 3.6

Documentation

Official documentation for LAD can be found at https://log-anomaly-detector.readthedocs.io/en/latest

Community

For help or questions about Log Anomaly Detector usage (e.g. "how do I do X?") then you can open an issue and mark it as question. One of our engineers would be glad to answer.

To report a bug, file a documentation issue, or submit a feature request, please open a GitHub issue.

For release announcements and other discussions, please subscribe to our mailing list (

https://groups.google.com/forum/#!members/aiops
)

Major updates will be presented at our AiOps special interest group meeting which is a part of openshift commons

OpenShift Commons AiOps Sig Calendar:

https://bit.ly/2lMn6yU

Contributing

We happily welcome contributions to LAD. Please see our contribution guide for details.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.