Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
1,224 detections
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This query visualises total emails with Spoof - DMARC fails summarizing the data daily.
This query visualises total emails with Phish (BEC) - Impersonation detections over time.
This query visualises total emails with Spoof - Composite Authentication fails summarizing the data daily.
This query visualises the total ammount of user false negative submissions from the junk folder
Identifies the top 10 users receiving inbound emails classified as malware, phishing, or spam.
Based on concepts from the Defender for Office 365 solution in Sentinel: https://techcommunity.microsoft.com/blog/microsoftdefenderforoffice365blog/part-2-build-custom-email-security-reports-and-dashboards-with-workbooks-in-micr/4411303
Based on concepts from the Defender for Office 365 solution in Sentinel: https://techcommunity.microsoft.com/blog/microsoftdefenderforoffice365blog/part-2-build-custom-email-security-reports-and-dashboards-with-workbooks-in-micr/4411303
This query visualises the amount of total Malware detections of files located on SharePoint, OneDrive and Teams, summarizing by the locations they are stored
This query visualises the total amount of spam emails that are quarantined, summarized by the detection method
This query visualises total files located on SharePoint, OneDrive and Teams with Malware detections over time summarizing the data daily.
This query visualises the daily amount of emails that had an admin post delivery action, summarizing the data by action type
'Identifies when Azure Run command is used to execute a PowerShell script on a VM that is unique.
The uniqueness of the PowerShell script is determined by taking a combined hash of the cmdLets it imports and the file size of the PowerShell script. Alerts from this detection indicate a unique PowerShell was executed in your environment.'
The uniqueness of the PowerShell script is determined by taking a combined hash of the cmdLets it imports and the file size of the PowerShell script. Alerts from this detection indicate a unique PowerShell was executed in your environment.'
'This creates an incident in the event a client generates excessive amounts of DNS queries for non-existent domains.
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM DNS schema'
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM DNS schema'
'Identifies IP addresses performing DNS lookups associated with common ToR proxies.
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM DNS schema'
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM DNS schema'
This query visualises total emails with Malware detections over time summarizing the data daily by Malware detection technologies/controls used for detecting unknown-unique malware.
'This query looks at correlating different reconnaissance alerts with interactive logon logs to help analysts investigate initial possible compromise activity'
'Matches domain name IOCs related to Forest Blizzard group activity published July 2019 with CommonSecurityLog, DnsEvents and VMConnection dataTypes.
References: https://blogs.microsoft.com/on-the-issues/2019/07/17/new-cyberthreats-require-new-ways-to-protect-democracy/.'
References: https://blogs.microsoft.com/on-the-issues/2019/07/17/new-cyberthreats-require-new-ways-to-protect-democracy/.'
'This rule identifies a web request with a user agent header known to belong to a hacking tool. This indicates a hacking tool is used on the host.<br>You can add custom hacking tool indicating User-Agent headers using a watchlist, for more information refer to the [UnusualUserAgents Watchlist](https://aka.ms/ASimUnusualUserAgentsWatchlist).
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM WebSession schema (ASIM WebSession Schema)'
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM WebSession schema (ASIM WebSession Schema)'
'Identifies clients with a high NXDomain count which could be indicative of a DGA (cycling through possible C2 domains where most C2s are not live). Alert is generated when a new IP address is seen (based on not being seen associated with
NXDomain records in prior 10-day baseline period).
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM DNS schema'
NXDomain records in prior 10-day baseline period).
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM DNS schema'
'Identifies evidence of brute force activity against a user based on multiple authentication failures and at least one successful authentication within a given time window.
Note that the query does not enforce any sequence, and does not require the successful authentication to occur last.
The default failure threshold is 10, success threshold is 1, and the default time window is 20 minutes.
To use this analytics rule, make sure you have deployed the [ASIM normalization parsers](https://aka.ms/ASimAuthentication)'
Note that the query does not enforce any sequence, and does not require the successful authentication to occur last.
The default failure threshold is 10, success threshold is 1, and the default time window is 20 minutes.
To use this analytics rule, make sure you have deployed the [ASIM normalization parsers](https://aka.ms/ASimAuthentication)'
'This query combines different Storage alerts with CommonSecurityLogs and StorageLogs helping analysts triage and investigate any
possible Storage related attacks faster thus reducing Mean Time To Respond'
possible Storage related attacks faster thus reducing Mean Time To Respond'
' This hunting query looks for process command line activity related to activity observed by Dev-0056.The command lines this query hunts for are used as part of the threat actor's post exploitation activity.
This query uses the Microsoft Sentinel Information Model - https://docs.microsoft.com/azure/sentinel/normalization'
This query uses the Microsoft Sentinel Information Model - https://docs.microsoft.com/azure/sentinel/normalization'
'This rule identifies communication with hosts that have a domain name that might have been generated by a Domain Generation Algorithm (DGA).
DGAs are used by malware to generate rendezvous points that are difficult to predict in advance. This detection uses the top 1 million domain names to build a model of what normal domains look like nad uses the model to identify domains that may have been randomly generated by an algorithm. You can modify the triThreshold and dgaLengthThreshold query parameters to change Analytic Rule sensitivity. The higher the numbers, the less noisy the rule is.
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM WebSession schema (ASIM WebSession Schema)'
DGAs are used by malware to generate rendezvous points that are difficult to predict in advance. This detection uses the top 1 million domain names to build a model of what normal domains look like nad uses the model to identify domains that may have been randomly generated by an algorithm. You can modify the triThreshold and dgaLengthThreshold query parameters to change Analytic Rule sensitivity. The higher the numbers, the less noisy the rule is.
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM WebSession schema (ASIM WebSession Schema)'
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