Metadata-Version: 2.1
Name: msticpy
Version: 1.7.0
Summary: MSTIC Security Tools
Home-page: https://github.com/microsoft/msticpy
Author: Ian Hellen
Author-email: ianhelle@microsoft.com
Maintainer: Pete Bryan
Maintainer-email: peter.bryan@microsoft.com
License: MIT License
Project-URL: Documentation, https://msticpy.readthedocs.io
Project-URL: Code, https://github.com/microsoft/msticpy
Project-URL: Issue tracker, https://github.com/microsoft/msticpy/issues
Description: # MSTIC Jupyter and Python Security Tools
        
        [![Build Status](https://dev.azure.com/mstic-detections/mstic-jupyter/_apis/build/status/microsoft.msticpy?branchName=main)](https://dev.azure.com/mstic-detections/mstic-jupyter/_build/latest?definitionId=14&branchName=main)
        [![Downloads](https://pepy.tech/badge/msticpy)](https://pepy.tech/project/msticpy)
        
        Microsoft Threat Intelligence Python Security Tools.
        
        **msticpy** is a library for InfoSec investigation and hunting
        in Jupyter Notebooks. It includes functionality to:
        
        - query log data from multiple sources
        - enrich the data with Threat Intelligence, geolocations and Azure
          resource data
        - extract Indicators of Activity (IoA) from logs and unpack encoded data
        - perform sophisticated analysis such as anomalous session detection and
          time series decomposition
        - visualize data using interactive timelines, process trees and
          multi-dimensional Morph Charts
        
        It also includes some time-saving notebook tools such as widgets to
        set query time boundaries, select and display items from lists, and
        configure the notebook environment.
        
        <img src="./docs/source/visualization/_static/Timeline-08.png"
        alt="Timeline" title="Msticpy Timeline Control" height="300" />
        
        The **msticpy** package was initially developed to support
        [Jupyter Notebooks](https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/)
        authoring for
        [Azure Sentinel](https://azure.microsoft.com/en-us/services/azure-sentinel/).
        While Azure Sentinel is still a big focus of our work, we are
        extending the data query/acquisition components to pull log data from
        other sources (currently Splunk, Microsoft Defender for Endpoint and
        Microsoft Graph are supported but we
        are actively working on support for data from other SIEM platforms).
        Most of the components can also be used with data from any source. Pandas
        DataFrames are used as the ubiquitous input and output format of almost
        all components. There is also a data provider to make it easy to and process
        data from local CSV files and pickled DataFrames.
        
        The package addresses three central needs for security investigators
        and hunters:
        
        - Acquiring and enriching data
        - Analyzing data
        - Visualizing data
        
        We welcome feedback, bug reports, suggestions for new features and contributions.
        
        ## Installing
        
        For core install:
        
        `pip install msticpy`
        
        If you are using *MSTICPy* with Azure Sentinel you should install with
        the "azsentinel" extra package:
        
        `pip install msticpy[azsentinel]`
        
        or for the latest dev build
        
        `pip install git+https://github.com/microsoft/msticpy`
        
        ## Upgrading
        
        To upgrade msticpy to the latest public non-beta release, run:
        
        `pip install --upgrade msticpy`
        
        Note it is good practice to copy your msticpyconfig.yaml and store it on your disk but outside of your msticpy folder, referencing it in an environment variable. This prevents you from losing your configurations every time you update your msticpy installation.
        
        ## Documentation
        
        Full documentation is at [ReadTheDocs](https://msticpy.readthedocs.io/en/latest/)
        
        Sample notebooks for many of the modules are in the
        [docs/notebooks](https://github.com/microsoft/msticpy/blob/master/docs/notebooks)
        folder and accompanying notebooks.
        
        You can also browse through the sample notebooks referenced at the end of this document
        to see some of the functionality used in context. You can play with some of the package
        functions in this interactive demo on mybinder.org.
        
        [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Azure/Azure-Sentinel-Notebooks/master?filepath=%2Fnbdemo%2Fmsticpy%20demo.ipynb)
        
        ---
        
        ## Log Data Acquisition
        
        QueryProvider is an extensible query library targeting Azure Sentinel/Log Analytics,
        Splunk, OData
        and other log data sources. It also has special support for
        [Mordor](https://github.com/OTRF/mordor) data sets and using local data.
        
        Built-in parameterized queries allow complex queries to be run
        from a single function call. Add your own queries using a simple YAML
        schema.
        
        [Data Queries Notebook](https://github.com/microsoft/msticpy/blob/master/docs/notebooks/Data_Queries.ipynb)
        
        ## Data Enrichment
        
        ### Threat Intelligence providers
        
        The TILookup class can lookup IoCs across multiple TI providers. built-in
        providers include AlienVault OTX, IBM XForce, VirusTotal and Azure Sentinel.
        
        The input can be a single IoC observable or a pandas DataFrame containing
        multiple observables. Depending on the provider, you may require an account
        and an API key. Some providers also enforce throttling (especially for free
        tiers), which might affect performing bulk lookups.
        
        [TIProviders](https://msticpy.readthedocs.io/en/latest/data_acquisition/TIProviders.html)
        and
        [TILookup Usage Notebook](https://github.com/microsoft/msticpy/blob/master/docs/notebooks/TIProviders.ipynb)
        
        ### GeoLocation Data
        
        The GeoIP lookup classes allow you to match the geo-locations of IP addresses
        using either:
        
        - GeoLiteLookup - Maxmind Geolite (see <https://www.maxmind.com>)
        - IPStackLookup  - IPStack (see <https://ipstack.com>)
        
        <img src="./docs/source/visualization/_static/FoliumMap-01.png"
          alt="Folium map"
          title="Plotting Geo IP Location" height="200" />
        
        [GeoIP Lookup](https://msticpy.readthedocs.io/en/latest/data_acquisition/GeoIPLookups.html)
        and
        [GeoIP Notebook](https://github.com/microsoft/msticpy/blob/master/docs/notebooks/GeoIPLookups.ipynb)
        
        ### Azure Resource Data, Storage and Azure Sentinel API
        
        
        The AzureData module contains functionality for enriching data regarding Azure host
        details with additional host details exposed via the Azure API. The AzureSentinel
        module allows you to query incidents, retrieve detector and hunting
        queries. AzureBlogStorage lets you read and write data from blob storage.
        
        [Azure Resource APIs](https://msticpy.readthedocs.io/en/latest/data_acquisition/AzureData.html),
        [Azure Sentinel APIs](https://msticpy.readthedocs.io/en/latest/data_acquisition/Sentinel.html),
        [Azure Storage](https://msticpy.readthedocs.io/en/latest/data_acquisition/AzureBlobStorage.html)
        ## Security Analysis
        
        This subpackage contains several modules helpful for working on security investigations and hunting:
        
        ### Anomalous Sequence Detection
        
        Detect unusual sequences of events in your Office, Active Directory or other log data.
        You can extract sessions (e.g. activity initiated by the same account) and identify and
        visualize unusual sequences of activity. For example, detecting an attacker setting
        a mail forwarding rule on someone's mailbox.
        
        [Anomalous Sessions](https://msticpy.readthedocs.io/en/latest/data_analysis/AnomalousSequence.html)
        and
        [Anomalous Sequence Notebook](https://github.com/microsoft/msticpy/blob/master/docs/notebooks/AnomalousSequence.ipynb)
        
        ### Time Series Analysis
        
        Time series analysis allows you to identify unusual patterns in your log data
        taking into account normal seasonal variations (e.g. the regular ebb and flow of
        events over hours of the day, days of the week, etc.). Using both analysis and
        visualization highlights unusual traffic flows or event activity for any data
        set.
        
        <img src="./docs/source/visualization/_static/TimeSeriesAnomalieswithRangeTool.png"
        alt="Time Series anomalies" title="Time Series anomalies" height="300" />
        
        [Time Series](https://msticpy.readthedocs.io/en/latest/visualization/TimeSeriesAnomalies.html)
        
        ## Visualization
        
        ### Event Timelines
        
        Display any log events on an interactive timeline. Using the
        [Bokeh Visualization Library](https://bokeh.org/) the timeline control enables
        you to visualize one or more event streams, interactively zoom into specific time
        slots and view event details for plotted events.
        
        <img src="./docs/source/visualization/_static/TimeLine-01.png"
        alt="Timeline" title="Msticpy Timeline Control" height="300" />
        
        [Timeline](https://msticpy.readthedocs.io/en/latest/visualization/EventTimeline.html)
        and
        [Timeline Notebook](https://github.com/microsoft/msticpy/blob/master/docs/notebooks/EventTimeline.ipynb)
        
        ### Process Trees
        
        The process tree functionality has two main components:
        
        - Process Tree creation - taking a process creation log from a host and building
          the parent-child relationships between processes in the data set.
        - Process Tree visualization - this takes the processed output displays an interactive process tree using Bokeh plots.
        
        There are a set of utility functions to extract individual and partial trees from the processed data set.
        
        <img src="./docs/source/visualization/_static/process_tree3.png"
        alt="Process Tree"
        title="Interactive Process Tree" height="400" />
        
        [Process Tree](https://msticpy.readthedocs.io/en/latest/visualization/ProcessTree.html)
        and
        [Process Tree Notebook](https://github.com/microsoft/msticpy/blob/master/docs/notebooks/ProcessTree.ipynb)
        
        ## Data Manipulation and Utility functions
        
        ### Pivot Functions
        
        Lets you use *MSTICPy* functionality in an "entity-centric" way.
        All functions, queries and lookups that relate to a particular entity type
        (e.g. Host, IpAddress, Url) are collected together as methods of that
        entity class. So, if you want to do things with an IP address, just load
        the IpAddress entity and browse its methods.
        
        [Pivot Functions](https://msticpy.readthedocs.io/en/latest/data_analysis/PivotFunctions.html)
        and
        [Pivot Functions Notebook](https://github.com/microsoft/msticpy/blob/master/docs/notebooks/PivotFunctions.ipynb)
        ### base64unpack
        
        Base64 and archive (gz, zip, tar) extractor. It will try to identify any base64 encoded
        strings and try decode them. If the result looks like one of the supported archive types it
        will unpack the contents. The results of each decode/unpack are rechecked for further
        base64 content and up to a specified depth.
        
        [Base64 Decoding](https://msticpy.readthedocs.io/en/latest/data_analysis/Base64Unpack.html)
        and
        [Base64Unpack Notebook](https://github.com/microsoft/msticpy/blob/master/docs/notebooks/Base64Unpack.ipynb)
        
        ### iocextract
        
        Uses regular expressions to look for Indicator of Compromise (IoC) patterns - IP Addresses, URLs,
        DNS domains, Hashes, file paths.
        Input can be a single string or a pandas dataframe.
        
        [IoC Extraction](https://msticpy.readthedocs.io/en/latest/data_analysis/IoCExtract.html)
        and
        [IoCExtract Notebook](https://github.com/microsoft/msticpy/blob/master/docs/notebooks/IoCExtract.ipynb)
        
        ### eventcluster (experimental)
        
        This module is intended to be used to summarize large numbers of
        events into clusters of different patterns. High volume repeating
        events can often make it difficult to see unique and interesting items.
        
        <img src="./docs/source/data_analysis/_static/EventClustering_2a.png"
          alt="Clustering"
          title="Clustering based on command-line variability" height="400" />
        
        This is an unsupervised learning module implemented using SciKit Learn DBScan.
        
        [Event Clustering](https://msticpy.readthedocs.io/en/latest/data_analysis/EventClustering.html)
        and
        [Event Clustering Notebook](https://github.com/microsoft/msticpy/blob/master/docs/notebooks/EventClustering.ipynb)
        
        ### auditdextract
        
        Module to load and decode Linux audit logs. It collapses messages sharing the same
        message ID into single events, decodes hex-encoded data fields and performs some
        event-specific formatting and normalization (e.g. for process start events it will
        re-assemble the process command line arguments into a single string).
        
        ### syslog_utils
        
        Module to support an investigation of a Linux host with only syslog logging enabled.
        This includes functions for collating host data, clustering logon events and detecting
        user sessions containing suspicious activity.
        
        ### cmd_line
        
        A module to support the detection of known malicious command line activity or suspicious
        patterns of command line activity.
        
        ### domain_utils
        
        A module to support investigation of domain names and URLs with functions to
        validate a domain name and screenshot a URL.
        
        ### Notebook widgets
        
        These are built from the [Jupyter ipywidgets](https://ipywidgets.readthedocs.io/) collection
        and group common functionality useful in InfoSec tasks such as list pickers,
        query time boundary settings and event display into an easy-to-use format.
        
        <img src="./docs/source/visualization/_static/Widgets1.png"
          alt="Time span Widget"
          title="Query time setter" height="100" />
        
        <img src="./docs/source/visualization/_static/Widgets4.png"
          alt="Alert browser"
          title="Alert browser" height="300" />
        
        ---
        
        ## More Notebooks on Azure Sentinel Notebooks GitHub
        
        [Azure Sentinel Notebooks](https://github.com/Azure/Azure-Sentinel-Notebooks)
        
        Example notebooks:
        
        - [Account Explorer](https://github.com/Azure/Azure-Sentinel-Notebooks/blob/master/Entity%20Explorer%20-%20Account.ipynb)
        - [Domain and URL Explorer](https://github.com/Azure/Azure-Sentinel-Notebooks/blob/master/Entity%20Explorer%20-%20Domain%20and%20URL.ipynb)
        - [IP Explorer](https://github.com/Azure/Azure-Sentinel-Notebooks/blob/master/Entity%20Explorer%20-%20IP%20Address.ipynb)
        - [Linux Host Explorer](https://github.com/Azure/Azure-Sentinel-Notebooks/blob/master/Entity%20Explorer%20-%20Linux%20Host.ipynb)
        - [Windows Host Explorer](https://github.com/Azure/Azure-Sentinel-Notebooks/blob/master/Entity%20Explorer%20-%20Windows%20Host.ipynb)
        
        View directly on GitHub or copy and paste the link into [nbviewer.org](https://nbviewer.jupyter.org/)
        
        ## Notebook examples with saved data
        
        See the following notebooks for more examples of the use of this package in practice:
        
        - Windows Alert Investigation in
          [GitHub](https://github.com/Azure/Azure-Sentinel-Notebooks/blob/master/Sample-Notebooks/Example%20-%20Guided%20Investigation%20-%20Process-Alerts.ipynb)
          or
          [NbViewer](https://nbviewer.jupyter.org/github/Azure/Azure-Sentinel-Notebooks/blob/master/Sample-Notebooks/Example%20-%20Guided%20Investigation%20-%20Process-Alerts.ipynb)
        - Office 365 Exploration in
          [GitHub](https://github.com/Azure/Azure-Sentinel-Notebooks/blob/master/Sample-Notebooks/Example%20-%20Guided%20Hunting%20-%20Office365-Exploring.ipynb)
          or [NbViewer](https://nbviewer.jupyter.org/github/Azure/Azure-Sentinel-Notebooks/blob/master/Sample-Notebooks/Example%20-%20Guided%20Hunting%20-%20Office365-Exploring.ipynb)
        - Cross-Network Hunting in
          [GitHub](https://github.com/Azure/Azure-Sentinel-Notebooks/blob/master/Sample-Notebooks/Example%20-%20Step-by-Step%20Linux-Windows-Office%20Investigation.ipynb)or
          [NbViewer](https://nbviewer.jupyter.org/github/Azure/Azure-Sentinel-Notebooks/blob/master/Sample-Notebooks/Example%20-%20Step-by-Step%20Linux-Windows-Office%20Investigation.ipynb)
        
        ## Supported Platforms and Packages
        
        - msticpy is OS-independent
        - Requires [Python 3.6 or later](https://www.python.org/dev/peps/pep-0494/)
        - See [requirements.txt](requirements.txt) for more details and version requirements.
        
        ---
        
        ## Contributing
        
        For (brief) developer guidelines, see this wiki article
        [Contributor Guidelines](https://github.com/microsoft/msticpy/wiki/Contributor-guidelines)
        
        This project welcomes contributions and suggestions.  Most contributions require you to agree to a
        Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
        the rights to use your contribution. For details, visit <https://cla.microsoft.com>.
        
        When you submit a pull request, a CLA-bot will automatically determine whether you need to provide
        a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions
        provided by the bot. You will only need to do this once across all repos using our CLA.
        
        This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
        For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
        contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
        
Keywords: security,azure,sentinel,mstic,cybersec,infosec,cyber,cybersecurity,jupyter,notebooks,SOC,hunting
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 5 - Production/Stable
Classifier: Framework :: IPython
Classifier: Framework :: Jupyter
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Topic :: Security
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: dev
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Provides-Extra: sumologic
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Provides-Extra: _azure_core
Provides-Extra: keyvault
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Provides-Extra: riskiq
Provides-Extra: all
Provides-Extra: azure
Provides-Extra: test
Provides-Extra: azsentinel
Provides-Extra: azuresentinel
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