Use Jupyter notebooks to hunt for security threats
The foundation of Azure Sentinel is the data store; it combines high performance querying, dynamic schema, and scales to massive data volumes. The Azure portal and all Azure Sentinel tools use a common API to access this data store. The same API is also available for external tools such as Jupyter notebooks and Python. While many common tasks can be carried out in the portal, Jupyter extends the scope of what you can do with this data. It combines full programmability with a huge collection of libraries for machine learning, visualization, and data analysis. These attributes make Jupyter a compelling tool for security investigation and hunting.
We've integrated the Jupyter experience into the Azure portal, making it easy for you to create and run notebooks to analyze your data. The Kqlmagic library provides the glue that lets you take queries from Azure Sentinel and run them directly inside a notebook. Queries use the Kusto Query Language. Several notebooks, developed by some of Microsoft's security analysts, are packaged with Azure Sentinel. Some of these notebooks are built for a specific scenario and can be used as-is. Others are intended as samples to illustrate techniques and features that you can copy or adapt for use in your own notebooks. Other notebooks may also be imported from the Azure Sentinel community GitHub.
The integrated Jupyter experience uses Azure Notebooks to store, share, and execute notebooks. You can also run these notebooks locally if you have a Python environment and Jupyter on your computer, or in other JupterHub environments such as Azure Databricks.
Notebooks have two components:
- The browser-based interface where you enter and run queries and code, and where the results of the execution are displayed.
- A kernel that is responsible for parsing and executing the code itself.
In Azure Notebooks, by default, this kernel runs on Azure Free Cloud Compute and Storage. If your notebooks include complex machine learning models or visualizations, consider using more powerful, dedicated compute resources such as Data Science Virtual Machines (DSVM). Notebooks in your account are kept private unless you choose to share them.
The Azure Sentinel notebooks use many popular Python libraries such as pandas, matplotlib, bokeh, and others. There are a great many other Python packages for you to choose from, covering areas such as:
- Visualizations and graphics
- Data processing and analysis
- Statistics and numerical computing
- Machine learning and deep learning
We've also released some open-source Jupyter security tools in a package named msticpy. This package is used in many of the included notebooks. Msticpy tools are designed specifically to help with creating notebooks for hunting and investigation and we're actively working on new features and improvements.
The initial notebooks include:
- Guided investigation - Process Alerts: Allows you to quickly triage alerts by analyzing activity on the affected host or hosts.
- Guided hunting - Windows host explorer: Allows you to explore account activity, process executions, network activity, and other events on a host.
- Guided hunting - Office365-Exploring: Hunt for suspicious Office 365 activity in multiple Office 365 data sets.
The Azure Sentinel Community GitHub repository is the location for any future Azure Sentinel notebooks built by Microsoft or contributed from the community.
To use the notebooks, you must have an Azure Notebooks account. For more information, see Quickstart: Sign in and set a user ID from the Azure Notebooks documentation. To create this account, you can use the Sign up for Azure Notebooks option from the command bar in Azure Sentinel - Notebooks:
You can run a notebook direct from Azure Sentinel, or clone all the Azure Sentinel notebooks to a new Azure Notebooks project.
Run a notebook from Azure Sentinel
From the Azure portal, navigate to Azure Sentinel > Threat management > Notebooks, where you can see notebooks that Azure Sentinel provides.
Select individual notebooks to read their descriptions, required data types, and data sources. For example:
Select the notebook you want to use, and then select Launch Notebook (Preview) to clone and configure the notebook into a new Azure Notebooks project that connects to your Azure Sentinel workspace. When the process is complete, the notebook opens within Azure Notebooks for you to run.
Clone Azure Sentinel notebooks to a new Azure Notebooks project
This procedure creates an Azure Notebooks project for you, which contains the Azure Sentinel notebooks. You can then run the notebooks as-is, or make changes to them and then run them.
From the Azure portal, navigate to Azure Sentinel > Threat management > Notebooks and then select Clone Notebooks from the command bar:
When the following dialog appears, select Import to clone the GitHub repo into your Azure Notebooks project. If you don't have an existing Azure Notebooks account, you'll be prompted to create one and sign in.
On the Upload GitHub Repository dialog box, don't select Clone recursively because this option refers to linked GitHub repos. For the project name, use the default name or type in a new one. Then click Import to start cloning the GitHub content, which can take a few minutes to complete.
Open the project you just created, and then open the Notebooks folder to see the notebooks. For example:
You can then run the notebooks from Azure Notebooks. To return to these notebooks from Azure Sentinel, select Go to your Notebooks from the command bar in Azure Sentinel - Notebooks:
Using notebooks to hunt
Each notebook walks you through the steps for carrying out a hunt or investigation. Libraries and other dependencies needed by the notebook can be installed from the notebook itself or via a simple configuration procedure. Configuration that ties your notebook project back to your Azure Sentinel subscription is automatically provisioned in the preceding steps.
If you're not already in Azure Notebooks, you can use the Go to your Notebooks option from the command bar in Azure Sentinel - Notebooks:
In Azure Notebooks, select My Projects, then the project that contains the Azure Sentinel notebooks, and finally the Notebooks folder.
Before you open a notebook, be aware that by default, Free Compute is selected to run the notebooks:
If you've configured a Data Science Virtual Machines (DSVM) to use as explained in the introduction, select the DSVM and authenticate before you open the first notebook.
Select a notebook to open it.
The first time you open a notebook, you might be prompted to select a kernel version. If you're not prompted, you can select the kernel version from Kernel > Change kernel, and then select a version that's at least 3.6. The selected kernel version is displayed in the top right of the notebook window:
Before you make any changes to notebook that you've downloaded, it's a good idea to make a copy of the original notebook and work on the copy. To do that, select File > Make a Copy. Working on copies lets you safely update to future versions of notebooks without overwriting any of your data.
You're now ready to run or edit the selected notebook.
For a quick introduction to querying data in Azure Sentinel, look at the GetStarted notebook in the main Notebooks folder.
You'll find additional sample notebooks in the Sample-Notebooks subfolder. These sample notebooks have been saved with data, so that it's easier to see the intended output. We recommend viewing these notebooks in nbviewer.
The HowTos folder contains notebooks describing, for example: Setting you default Python version, configuring a DSVM, creating Azure Sentinel bookmarks from a notebook, and other subjects.
The notebooks provided are intended as both useful tools and as illustrations and code samples that you can use in the development of your own notebooks.
We welcome feedback, whether suggestions, requests for features, contributed Notebooks, bug reports or improvements and additions to existing notebooks. Go to the Azure Sentinel Community GitHub to create an issue or fork and upload a contribution.
In this article, you learned how to get started using Jupyter notebooks in Azure Sentinel. To learn more about Azure Sentinel, see the following articles: