Azure Sentinel
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'This hunting query will help detect successful sign-ins from devices that are marked non-compliant along with bulk download activity.
Attackers may attempt to get a list of accounts, groups, registration details within an environment which could help in follow-on behavior.
Best practice is to block sign ins from non-complaint devices, however if allowed monitor these events to ensure they do not lead to other risky activity.
Reference: https://docs.microsoft.com/azure/active-directory/fundamentals/security-operations-devices#non-compliant-device-sign-in'
Attackers may attempt to get a list of accounts, groups, registration details within an environment which could help in follow-on behavior.
Best practice is to block sign ins from non-complaint devices, however if allowed monitor these events to ensure they do not lead to other risky activity.
Reference: https://docs.microsoft.com/azure/active-directory/fundamentals/security-operations-devices#non-compliant-device-sign-in'
'Looks for multiple users that had their admin role removed by a single user within a certain period.
The default threshold is 5 removals, this can be edited in the query.'
The default threshold is 5 removals, this can be edited in the query.'
'This query searches for failed attempts to log in from more than 15 various users within a 5 minute timeframe from the same source. This is a potential indication of a password spray attack
To use this analytics rule, make sure you have deployed the [ASIM normalization parsers](https://aka.ms/ASimAuthentication)'
To use this analytics rule, make sure you have deployed the [ASIM normalization parsers](https://aka.ms/ASimAuthentication)'
Listing Email Remediation Actions performed via Explorer in Defender for Office 365
Graph of MDO detections trended over time
Detect Active Directory LDAP queries that search for users with comment or description that contains the string "pass" that might suggest for the user password
This LDAP query cover MetaSploit - enum_ad_user_comments tool
This LDAP query cover MetaSploit - enum_ad_user_comments tool
'Any Alerts that fired related to a given IpAddress during the range of +6h and -3d'
This query was originally published in the threat analytics report, Ryuk ransomware. There is also a related blog.
Ryuk is human-operated ransomware. Much like DoppelPaymer ransomware, Ryuk is spread manually, often on networks that are already infected with Trickbot.
During the earliest stages of a Ryuk infection, an operator downloads Cobalt Strike, a penetration testing kit that is also used by malicious actors. Cobalt Strike is used by Ryuk operators to explore the network before deploying the Ryuk payload. This malicious behavior is often obscured by Base64 encoding and other tricks.
The following query detects possible invocation of Cobalt Strike using Windows Management Instrumentation (WMI).
The See also section below lists links to other queries associated with Ryuk ransomware.
References:
https://www.microsoft.com/security/blog/2020/03/05/human-operated-ransomware-attacks-a-preventable-disaster/
https://www.microsoft.com/wdsi/threats/malware-encyclopedia-description?Name=Ransom:Win32/Ryuk&threatId=-2147232689
https://www.microsoft.com/security/blog/2020/03/05/human-operated-ransomware-attacks-a-preventable-disaster/
https://www.cobaltstrike.com/
https://docs.microsoft.com/windows/win32/wmisdk/wmi-start-page
Ryuk is human-operated ransomware. Much like DoppelPaymer ransomware, Ryuk is spread manually, often on networks that are already infected with Trickbot.
During the earliest stages of a Ryuk infection, an operator downloads Cobalt Strike, a penetration testing kit that is also used by malicious actors. Cobalt Strike is used by Ryuk operators to explore the network before deploying the Ryuk payload. This malicious behavior is often obscured by Base64 encoding and other tricks.
The following query detects possible invocation of Cobalt Strike using Windows Management Instrumentation (WMI).
The See also section below lists links to other queries associated with Ryuk ransomware.
References:
https://www.microsoft.com/security/blog/2020/03/05/human-operated-ransomware-attacks-a-preventable-disaster/
https://www.microsoft.com/wdsi/threats/malware-encyclopedia-description?Name=Ransom:Win32/Ryuk&threatId=-2147232689
https://www.microsoft.com/security/blog/2020/03/05/human-operated-ransomware-attacks-a-preventable-disaster/
https://www.cobaltstrike.com/
https://docs.microsoft.com/windows/win32/wmisdk/wmi-start-page
'This hunting query looks for queries that appear to be looking for secrets or passwords in tables.'
Expanding on DeviceEvents output with Attack Surface Reduction (ASR) rule descriptions.
The ActionType values of the ASR events already explain what rule was matched and if it was audited or blocked.
However, it could still be useful to have a more human-friendly description in the results.
Also, this query is a good example for how you could define your own lookup tables and join with them.
The events in the DeviceEvents table contain a GUID for the various ASR rules rather than a full description of the rule.
This query will create a table which has the description for each ASR rule as per https://docs.microsoft.com/windows/security/threat-protection/windows-defender-exploit-guard/enable-attack-surface-reduction.
This table is then joined to the output of a query against the DeviceEvents table and shows a summary count of the events by the newly defined description.
This query shows the ability to use joins and custom dimension tables.
See https://docs.loganalytics.io/docs/Language-Reference/Tabular-operators/join-operator for more information on the join syntax.
For more questions on this query, feel free to ping @FlyingBlueMonki on twitter or mattegen@microsoft.com via email.
First lets start by creating a table of the rule descriptions to rule guids.
The ActionType values of the ASR events already explain what rule was matched and if it was audited or blocked.
However, it could still be useful to have a more human-friendly description in the results.
Also, this query is a good example for how you could define your own lookup tables and join with them.
The events in the DeviceEvents table contain a GUID for the various ASR rules rather than a full description of the rule.
This query will create a table which has the description for each ASR rule as per https://docs.microsoft.com/windows/security/threat-protection/windows-defender-exploit-guard/enable-attack-surface-reduction.
This table is then joined to the output of a query against the DeviceEvents table and shows a summary count of the events by the newly defined description.
This query shows the ability to use joins and custom dimension tables.
See https://docs.loganalytics.io/docs/Language-Reference/Tabular-operators/join-operator for more information on the join syntax.
For more questions on this query, feel free to ping @FlyingBlueMonki on twitter or mattegen@microsoft.com via email.
First lets start by creating a table of the rule descriptions to rule guids.
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