Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects the Internet Information Services (IIS) command-line tool, AppCmd, being used to list passwords
// Detect multiple sensitive Active Directory LDAP queries made in bin time
// Sensitive queries defined as Roasting or sensitive objects queries
// Replace 10 on line 6 with your desired thershold
// Replace 1m on line 7 with your desired bin time
// This LDAP query cover Rubeus, Kerberoast, BloodHound tools
// Sensitive queries defined as Roasting or sensitive objects queries
// Replace 10 on line 6 with your desired thershold
// Replace 1m on line 7 with your desired bin time
// This LDAP query cover Rubeus, Kerberoast, BloodHound tools
Detects suspicious commands that could be related to activity that uses volume shadow copy to steal and retrieve hashes from the NTDS.dit file remotely
Detects a suspicious process creation as SYSTEM user (suspicious program or command line parameter)
Detects suspicious commands that could be related to activity that uses volume shadow copy to steal and retrieve hashes from the NTDS.dit file remotely
Detects Invoke-Mimikatz PowerShell script and alike. Mimikatz is a credential dumper capable of obtaining plaintext Windows account logins and passwords.
Conti recommendation to its affiliates to use esentutl to access NTDS dumped file. Trickbot also uses this utilities to get MSEdge info via its module pwgrab.
Detects using Rpcping.exe to send a RPC test connection to the target server (-s) and force the NTLM hash to be sent in the process.
Detects a suspicious LSASS process process clone that could be a sign of credential dumping activity
Detects default filenames output from the execution of CrackMapExec and Impacket-secretsdump against an endpoint.
'Identifies an export of the ADFS DKM Master Key from Active Directory.
References: https://blogs.microsoft.com/on-the-issues/2020/12/13/customers-protect-nation-state-cyberattacks/,
https://www.fireeye.com/blog/threat-research/2020/12/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor.html?1
To understand further the details behind this detection, please review the details in the original PR and subequent PR update to this:
https://github.com/Azure/Azure-Sentinel/pull/1562#issue-551542469
https://github.com/Azure/Azure-Sentinel/pull/1512#issue-543053339
'
References: https://blogs.microsoft.com/on-the-issues/2020/12/13/customers-protect-nation-state-cyberattacks/,
https://www.fireeye.com/blog/threat-research/2020/12/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor.html?1
To understand further the details behind this detection, please review the details in the original PR and subequent PR update to this:
https://github.com/Azure/Azure-Sentinel/pull/1562#issue-551542469
https://github.com/Azure/Azure-Sentinel/pull/1512#issue-543053339
'
Detects the execution of the hacktool Rubeus via PE information of command line parameters
Detects the execution of the hacktool Rubeus using specific command line flags
Detects the enumeration of a specific DLL or EXE being used by a binary via "tasklist.exe".
This is often used by attackers in order to find the specific process identifier (PID) that is using the DLL in question.
In order to dump the process memory or perform other nefarious actions.
This is often used by attackers in order to find the specific process identifier (PID) that is using the DLL in question.
In order to dump the process memory or perform other nefarious actions.
Detects wceaux.dll access while WCE pass-the-hash remote command execution on source host
'Identifies a match across various data feeds for hashes and IP IOC related to Europium
Reference: https://www.microsoft.com/security/blog/2022/09/08/microsoft-investigates-iranian-attacks-against-the-albanian-government'
Reference: https://www.microsoft.com/security/blog/2022/09/08/microsoft-investigates-iranian-attacks-against-the-albanian-government'
Detects usage of a PowerShell command to dump the live memory of a Windows machine
Detects known malicious service installs that only appear in cases of lateral movement, credential dumping, and other suspicious activities.
Detects a highly relevant Antivirus alert that reports a password dumper.
This event must not be ignored just because the AV has blocked the malware but investigate, how it came there in the first place.
This event must not be ignored just because the AV has blocked the malware but investigate, how it came there in the first place.
This query will hunt for files matching the current abuse.ch recent threat feed based on Sha256. Currently the query is set up to analyze the last day worth of events, but this is configurable using the MaxAge variable.
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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