ブログはじめました。日本語だと不便です。機能も少ないです。 なのでそのうち他のサービスを探します。ここはとりあえず実験的に。

木曜日, 1月 25, 2024

Msticpy - Microsoft Threat Intelligence Security Tools

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.



The msticpy package was initially developed to support Jupyter Notebooks authoring for 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


Documentation

Full documentation is at ReadTheDocs

Sample notebooks for many of the modules are in the 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.


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 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


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 and TILookup Usage Notebook


GeoLocation Data

The GeoIP lookup classes allow you to match the geo-locations of IP addresses using either:

GeoIP Lookup and GeoIP Notebook


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, Azure Sentinel APIs, Azure Storage


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 and Anomalous Sequence Notebook


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.


Time Series


Visualization

Event Timelines

Display any log events on an interactive timeline. Using the Bokeh Visualization Library 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.


Timeline and Timeline Notebook


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.


Process Tree and Process Tree Notebook


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 and Pivot Functions Notebook


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 and Base64Unpack Notebook


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 and IoCExtract Notebook


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.



This is an unsupervised learning module implemented using SciKit Learn DBScan.

Event Clustering and Event Clustering Notebook


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 he 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 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.


 



More Notebooks on Azure Sentinel Notebooks GitHub

Azure Sentinel Notebooks

Example notebooks:

View directly on GitHub or copy and paste the link into nbviewer.org


Notebook examples with saved data

See the following notebooks for more examples of the use of this package in practice:


Supported Platforms and Packages

Contributing

For (brief) developer guidelines, see this wiki article 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.



Related news

  1. Hack Tool Apk
  2. Pentest Tools Website
  3. Hacking Tools For Kali Linux
  4. Hacker Tools For Ios
  5. Hacker Techniques Tools And Incident Handling
  6. Hacking Tools For Windows 7
  7. Pentest Recon Tools
  8. Hacking Tools For Beginners
  9. Hacking Apps
  10. Best Hacking Tools 2019
  11. Pentest Tools Free
  12. Termux Hacking Tools 2019
  13. Computer Hacker
  14. Hack Tools
  15. Hacking Tools Mac
  16. Kik Hack Tools
  17. Pentest Tools Bluekeep
  18. World No 1 Hacker Software
  19. Hack Tools
  20. Hack And Tools
  21. World No 1 Hacker Software
  22. Pentest Tools Linux
  23. Hacker Tools Github
  24. Pentest Tools List
  25. Hack Tool Apk No Root
  26. Hacking Tools Name
  27. Underground Hacker Sites
  28. Pentest Tools Tcp Port Scanner
  29. Hacking Tools For Windows Free Download
  30. New Hack Tools
  31. Hacking Tools For Beginners
  32. Best Hacking Tools 2020
  33. Tools 4 Hack
  34. Hack Website Online Tool
  35. Pentest Tools Url Fuzzer
  36. Android Hack Tools Github
  37. Hacking Tools Download
  38. Hacker Tools Free Download
  39. Hacking Tools For Windows
  40. Hacker Tools
  41. Hacker Tools For Windows
  42. Hack Tools Online
  43. Hack Tool Apk No Root
  44. How To Install Pentest Tools In Ubuntu
  45. Hacking Tools Windows 10
  46. Easy Hack Tools
  47. Nsa Hacker Tools
  48. Hacker Tools Github
  49. Hacker Tools Hardware
  50. Pentest Tools Kali Linux
  51. Hack Tools Download
  52. Best Hacking Tools 2020
  53. Pentest Tools Tcp Port Scanner
  54. Pentest Tools Download
  55. Nsa Hack Tools
  56. Hacker Tools Windows
  57. Hack Tool Apk No Root
  58. Hacker
  59. Hacks And Tools
  60. Hack Tool Apk
  61. Underground Hacker Sites
  62. Hackers Toolbox
  63. Hacking Tools For Games
  64. Pentest Tools Online
  65. Hacker Tools 2019
  66. Pentest Tools Find Subdomains
  67. Black Hat Hacker Tools
  68. Hacking Tools For Windows 7
  69. Pentest Box Tools Download
  70. Pentest Tools Android
  71. Growth Hacker Tools
  72. Hacking Tools 2020
  73. Hacking Tools Software
  74. Hacking Tools For Windows
  75. Hack Tools Online
  76. Hacker Hardware Tools
  77. Hack Tools For Mac
  78. Pentest Tools Framework
  79. Pentest Box Tools Download
  80. Hacker Tools For Mac
  81. Wifi Hacker Tools For Windows
  82. Hack Tools
  83. Pentest Tools Port Scanner
  84. Pentest Tools Website
  85. Hack Tools For Windows
  86. Hacking Tools Github
  87. Best Hacking Tools 2019
  88. Hacking Tools Hardware
  89. Pentest Tools
  90. Pentest Tools For Windows
  91. Hack Website Online Tool
  92. Wifi Hacker Tools For Windows
  93. Hacker
  94. Hack Apps
  95. Hacking App
  96. Hak5 Tools
  97. World No 1 Hacker Software
  98. Hack Tool Apk
  99. Hacker Tools Windows
  100. Hacker Tools Linux
  101. Hacker Tools 2019
  102. Hack Tools For Pc
  103. Pentest Tools Url Fuzzer
  104. Hacking Tools Usb
  105. Hacking Tools And Software
  106. Pentest Tools Website Vulnerability
  107. Hacker Tools List
  108. Hacker Tool Kit
  109. Pentest Tools Url Fuzzer
  110. Hacker Tools Mac
  111. Hacking Tools For Mac
  112. Free Pentest Tools For Windows
  113. Hacker
  114. Blackhat Hacker Tools
  115. Hacker Tools Hardware