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:

• GeoLiteLookup - Maxmind Geolite (see https://www.maxmind.com)


• IPStackLookup - IPStack (see https://ipstack.com)

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:

• Account Explorer


• Domain and URL Explorer


• IP Explorer


• Linux Host Explorer


• Windows Host Explorer

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:

• Windows Alert Investigation in
  GitHub
  or
  NbViewer


• Office 365 Exploration in
  GitHub
  or NbViewer


• Cross-Network Hunting in
  GitHubor
  NbViewer

Supported Platforms and Packages

• msticpy is OS-independent


• Requires Python 3.6 or later


• See requirements.txt for more details and version requirements.

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.

Source: OffensiveSec
Source Link: https://offensivesec.blogspot.com/2022/02/microsoft-threat-intelligence-python.html