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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.
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.
To develop a custom hunting query, follow these steps:
Here are some examples of custom hunting queries that could be relevant for a security analyst using Azure Sentinel:
SigninLogs
| where TimeGenerated > ago(1d)
| where CountryOrRegion != “ExpectedCountry” and UserPrincipalName == “[email protected]”
| project TimeGenerated, UserPrincipalName, IPAddress, CountryOrRegion
This query helps in identifying potentially malicious sign-in attempts from geographical locations that are different from a user’s usual login pattern.
SigninLogs
| where TimeGenerated > ago(1h)
| summarize CountFailedLogins = count() by UserPrincipalName
| where CountFailedLogins > 5
| project UserPrincipalName, CountFailedLogins
With this query, the analyst looks for accounts with more than five failed login attempts in the last hour, indicating a possible brute-force attack.
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.
Explanation: KQL is the language used for constructing queries, including custom hunting queries, in Azure Sentinel and other Microsoft security tools.
Explanation: Custom hunting queries can be run manually or scheduled to run at regular intervals.
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.
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.
Explanation: Custom hunting queries can be shared among team members within the same organization to collaborate on security hunting efforts.
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.
Explanation: Custom hunting queries can be edited or deleted as needed to refine or adjust hunting strategies.
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.
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.
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.
Custom hunting queries are custom log searches created by security analysts in Microsoft Sentinel to search for suspicious or anomalous activities.
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.
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.
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.
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.
Yes, custom hunting queries can be scheduled to run automatically in Microsoft Sentinel using the Scheduled Hunting feature.
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.
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.
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.
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.
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