Tutorial / Cram Notes
They enable analysts to explore, identify, and isolate threats that may not be detected by automated security tools. The SC-200 Microsoft Security Operations Analyst exam covers the skills needed to create and manage these custom hunting queries using tools such as Azure Sentinel.
Understanding Hunting Queries
Hunting queries are traditionally written in a query language such as Kusto Query Language (KQL) for Azure Sentinel. They are used to search across various data sources and logs to find actions that might indicate malicious activity or security threats. When creating these queries, it’s essential to have a clear understanding of the data schema and the types of activities or events you are searching for.
Developing Custom Hunting Queries
To develop a custom hunting query, follow these steps:
- Identify the Objective: Define what you’re looking for, whether it’s a sign of a particular attack, anomalous behavior, or indicators of compromise (IoCs).
- Understand the Data: Know what data you have available. This includes logs from firewalls, intrusion detection systems, Windows Defender, Azure Activity logs, and other sources.
- Draft the Query: Use KQL to construct your query. Begin with a broad search and then refine it to isolate the specific events of interest.
- Test and Iterate: Run the query and examine the results. Adjust the query as necessary to filter out noise and improve accuracy.
- Validate Findings: Compare the results with baseline behavior or known IoCs to confirm if the activity is indeed suspicious.
- Save and Operationalize: Once a query is validated, it can be saved for future use and potentially automated through alerts or incorporated into dashboards.
Examples of Custom Hunting Queries
Here are some examples of custom hunting queries that could be relevant for a security analyst using Azure Sentinel:
- Example 1: Unusual Login Locations
SigninLogs
| where TimeGenerated > ago(1d)
| where CountryOrRegion != “ExpectedCountry” and UserPrincipalName == “[email protected]”
| project TimeGenerated, UserPrincipalName, IPAddress, CountryOrRegionThis query helps in identifying potentially malicious sign-in attempts from geographical locations that are different from a user’s usual login pattern.
- Example 2: Numerous Failed Login Attempts
SigninLogs
| where TimeGenerated > ago(1h)
| summarize CountFailedLogins = count() by UserPrincipalName
| where CountFailedLogins > 5
| project UserPrincipalName, CountFailedLoginsWith this query, the analyst looks for accounts with more than five failed login attempts in the last hour, indicating a possible brute-force attack.
Custom Query Tips
- Performance Optimizations: Use filters to reduce the dataset as early as possible in the query.
- Pivot Wisely: Pivoting from one dataset to another is useful but should be done judiciously to avoid performance impacts.
- Use Timeframes: Overly broad timeframes can lead to excessive data and potential performance hits. Be as specific as possible.
- Annotations: Add comments to your queries to explain their purpose and logic for future reference.
In conclusion, being skilled in creating custom hunting queries is pivotal for an analyst aiming to pass the SC-200 exam and for practical threat hunting. These custom queries not only enhance the ability to detect and respond to threats but also empower security operations with tailored investigation capabilities. Using examples and understanding optimization tips can lead to more effective and efficient hunting on platforms such as Azure Sentinel.
Practice Test with Explanation
T/F: Custom hunting queries can be created using the Kusto Query Language (KQL).
- Answer: True
Explanation: KQL is the language used for constructing queries, including custom hunting queries, in Azure Sentinel and other Microsoft security tools.
T/F: Custom hunting queries can only be run manually in Microsoft Defender for Endpoint.
- Answer: False
Explanation: Custom hunting queries can be run manually or scheduled to run at regular intervals.
Which of the following data sources can be used in creating custom hunting queries? (Select all that apply)
- A) Azure Activity Logs
- B) Windows Event Logs
- C) Network Traffic
- D) Machine Learning Models
Answer: A) Azure Activity Logs, B) Windows Event Logs, C) Network Traffic
Explanation: Azure Activity Logs, Windows Event Logs, and Network Traffic are all data sources that can be utilized in custom hunting queries.
When creating a custom hunting query, it is recommended to:
- A) Use broad time ranges for better performance
- B) Limit the scope of the query to target specific events
- C) Avoid filtering results
- D) Exclude time constraints for comprehensiveness
Answer: B) Limit the scope of the query to target specific events
Explanation: Limiting the scope of the query helps focus on specific events, making the hunting process more efficient and relevant.
T/F: Custom hunting queries can be shared with other analysts within the same organization.
- Answer: True
Explanation: Custom hunting queries can be shared among team members within the same organization to collaborate on security hunting efforts.
What is the primary purpose of creating custom hunting queries in Microsoft security solutions?
- A) To schedule software updates
- B) To automate the response to incidents
- C) To proactively search for potential threats
- D) To generate compliance reports
Answer: C) To proactively search for potential threats
Explanation: The primary purpose of custom hunting queries is to proactively search and identify potential threats that may not be detected by automated security tools.
T/F: After creating a custom hunting query, it cannot be edited or deleted.
- Answer: False
Explanation: Custom hunting queries can be edited or deleted as needed to refine or adjust hunting strategies.
When creating custom hunting queries, you should:
- A) Use generic terms to ensure broad matching
- B) Test queries to ensure accuracy and relevance
- C) Ignore query performance for the sake of thoroughness
- D) Avoid documenting the query purpose
Answer: B) Test queries to ensure accuracy and relevance
Explanation: Testing queries is important to confirm that they are accurate and relevant to the threats you are trying to detect.
Which of the following should be considered when creating custom hunting queries? (Select all that apply)
- A) The volume of data being queried
- B) Scheduling the query to run during off-peak hours
- C) The potential impact of the query on system performance
- D) Using the latest language features without testing
Answer: A) The volume of data being queried, B) Scheduling the query to run during off-peak hours, C) The potential impact of the query on system performance
Explanation: It is important to consider the volume of data, timing of the query execution, and impact on system performance to prevent disruptions and ensure efficient hunting.
A successful custom hunting query should:
- A) Return all log entries for comprehensive analysis
- B) Produce actionable insights with minimal noise
- C) Be constructed without regard to data retention policies
- D) Rely on default settings for simplicity
Answer: B) Produce actionable insights with minimal noise
Explanation: A good custom hunting query should produce actionable insights while minimizing irrelevant data (noise) to focus on potential threats.
Interview Questions
What are custom hunting queries in Microsoft Sentinel?
Custom hunting queries are custom log searches created by security analysts in Microsoft Sentinel to search for suspicious or anomalous activities.
How can you create a custom hunting query in Microsoft Sentinel?
You can create a custom hunting query in Microsoft Sentinel by navigating to the Hunting pane, selecting a data source, specifying the query language and syntax, and running the query.
What are the benefits of creating custom hunting queries in Microsoft Sentinel?
Creating custom hunting queries in Microsoft Sentinel can help you identify potential security threats that may not be detected by built-in analytics rules or alerts.
What are some examples of custom hunting queries that can be created in Microsoft Sentinel?
Some examples of custom hunting queries that can be created in Microsoft Sentinel include queries to detect lateral movement, suspicious process activity, or malicious file downloads.
How can you share custom hunting queries in Microsoft Sentinel?
You can share custom hunting queries in Microsoft Sentinel by exporting the query to a JSON file and then importing it into another Microsoft Sentinel workspace.
Can custom hunting queries be scheduled to run automatically in Microsoft Sentinel?
Yes, custom hunting queries can be scheduled to run automatically in Microsoft Sentinel using the Scheduled Hunting feature.
How can you validate the results of a custom hunting query in Microsoft Sentinel?
You can validate the results of a custom hunting query in Microsoft Sentinel by reviewing the search results, analyzing the relevant fields, and verifying that the query has identified any potential security threats.
What are some best practices for creating custom hunting queries in Microsoft Sentinel?
Some best practices for creating custom hunting queries in Microsoft Sentinel include starting with a specific use case, using relevant data sources, defining clear criteria, and testing the query thoroughly before deploying it in a production environment.
What is the difference between a custom hunting query and a detection rule in Microsoft Sentinel?
A custom hunting query is a log search that is created by a security analyst to search for potential security threats, while a detection rule is a pre-defined set of conditions that automatically generate an alert when a threat is detected.
How can you monitor the performance and efficiency of custom hunting queries in Microsoft Sentinel?
You can monitor the performance and efficiency of custom hunting queries in Microsoft Sentinel by tracking the number of times the query is executed, the duration of the search, and the number of results returned. This can be done using the Hunting Metrics workbook or other custom dashboards.
Great post! I was struggling with creating custom hunting queries for SC-200 until I read this.
Can anyone share some tips for optimizing performance in custom hunting queries?
I find using the ‘let’ statement in KQL very useful. Does anyone else use this in their queries?
I’m having trouble understanding how to use joins in Kusto Query Language. Could someone explain?
Thanks for compiling this post. Very helpful!
Does anyone know how to export the results of a custom hunting query for further analysis?
This post didn’t cover much about dynamic data types in KQL. Any advice?
This article was quite informative, thanks!