`.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1105"]}
-known_false_positives = Limited false positives in most environments, however tune as needed based on parent-child relationship or network connection.
-providing_technologies = []
-
-[savedsearch://ESCU - CertUtil With Decode Argument - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = CertUtil.exe may be used to `encode` and `decode` a file, including PE and script code. Encoding will convert a file to base64 with `-----BEGIN CERTIFICATE-----` and `-----END CERTIFICATE-----` tags. Malicious usage will include decoding a encoded file that was downloaded. Once decoded, it will be loaded by a parallel process. Note that there are two additional command switches that may be used - `encodehex` and `decodehex`. Similarly, the file will be encoded in HEX and later decoded for further execution. During triage, identify the source of the file being decoded. Review its contents or execution behavior for further analysis.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1140"]}
-known_false_positives = Typically seen used to `encode` files, but it is possible to see legitimate use of `decode`. Filter based on parent-child relationship, file paths, endpoint or user.
-providing_technologies = []
-
-[savedsearch://ESCU - Certutil exe certificate extraction - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for arguments to certutil.exe indicating the manipulation or extraction of Certificate. This certificate can then be used to sign new authentication tokens specially inside Federated environments such as Windows ADFS.
-how_to_implement =
-annotations = {"kill_chain_phases": ["Installation"]}
-known_false_positives = Unless there are specific use cases, manipulating or exporting certificates using certutil is uncommon. Extraction of certificate has been observed during attacks such as Golden SAML and other campaigns targeting Federated services.
-providing_technologies = []
-
-[savedsearch://ESCU - Child Processes of Spoolsv exe - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM.
-how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro to filter out legitimate child processes spawned by spoolsv.exe.
-annotations = {"cis20": ["CIS 5", "CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["PR.AC", "PR.PT", "DE.CM"]}
-known_false_positives = Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search.
-providing_technologies = []
-
-[savedsearch://ESCU - Clients Connecting to Multiple DNS Servers - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search.
-how_to_implement = This search requires that DNS data is being ingested and populating the `Network_Resolution` data model. This data can come from DNS logs or from solutions that parse network traffic for this data, such as Splunk Stream or Bro.\
-This search produces fields (`dest_count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Distinct DNS Connections, **Field:** dest_count\
-Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`
-annotations = {"cis20": ["CIS 9", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048.003"], "nist": ["PR.PT", "DE.AE", "PR.DS"]}
-known_false_positives = It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate.
-providing_technologies = []
-
-[savedsearch://ESCU - Clop Common Exec Parameter - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = The following analytics are designed to identifies some CLOP ransomware variant that using arguments to execute its main code or feature of its code. In this variant if the parameter is "runrun", CLOP ransomware will try to encrypt files in network shares and if it is "temp.dat", it will try to read from some stream pipe or file start encrypting files within the infected local machines. This technique can be also identified as an anti-sandbox technique to make its code non-responsive since it is waiting for some parameter to execute properly.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"kill_chain_phases": ["Obfuscation"], "mitre_attack": ["T1204"]}
-known_false_positives = Operators can execute third party tools using these parameters.
-providing_technologies = []
-
-[savedsearch://ESCU - Clop Ransomware Known Service Name - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = This detection is to identify the common service name created by the CLOP ransomware as part of its persistence and high privilege code execution in the infected machine. Ussually CLOP ransomware use StartServiceCtrlDispatcherW API in creating this service entry.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.
-annotations = {"kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1543"]}
-known_false_positives = unknown
-providing_technologies = []
-
[savedsearch://ESCU - Cloud API Calls From Previously Unseen User Roles - Rule]
type = detection
asset_type = AWS Instance
@@ -1772,16 +443,6 @@ annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.A
known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior.
providing_technologies = []
-[savedsearch://ESCU - Cloud Network Access Control List Deleted - Rule]
-type = detection
-asset_type = Instance
-confidence = medium
-explanation = Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the Change datamodel to detect users deleting network ACLs. Deprecated because it's a duplicate
-how_to_implement = You must be ingesting your cloud infrastructure logs from your cloud provider. You can also provide additional filtering for this search by customizing the `cloud_network_access_control_list_deleted_filter` macro.
-annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]}
-known_false_positives = It's possible that a user has legitimately deleted a network ACL.
-providing_technologies = []
-
[savedsearch://ESCU - Cloud Provisioning Activity From Previously Unseen City - Rule]
type = detection
asset_type = AWS Instance
@@ -1826,233 +487,6 @@ known_false_positives = This is a strictly behavioral search, so we define "fals
This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you.
providing_technologies = []
-[savedsearch://ESCU - Cobalt Strike Named Pipes - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies the use of default or publicly known named pipes used with Cobalt Strike. A named pipe is a named, one-way or duplex pipe for communication between the pipe server and one or more pipe clients. Cobalt Strike uses named pipes in many ways and has default values used with the Artifact Kit and Malleable C2 Profiles. The following query assists with identifying these default named pipes. Each EDR product presents named pipes a little different. Consider taking the values and generating a query based on the product of choice. \
-Upon triage, review the process performing the named pipe. If it is explorer.exe, It is possible it was injected into by another process. Review recent parallel processes to identify suspicious patterns or behaviors. A parallel process may have a network connection, review and follow the connection back to identify any file modifications.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1055"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = The idea of using named pipes with Cobalt Strike is to blend in. Therefore, some of the named pipes identified and added may cause false positives. Filter by process name or pipe name to reduce false positives.
-providing_technologies = []
-
-[savedsearch://ESCU - Common Ransomware Extensions - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The search looks for file modifications with extensions commonly used by Ransomware
-how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.\
-This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Name, **Field:** Name\
-1. \
-1. **Label:** File Extension, **Field:** file_extension\
-Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1485"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions.
-providing_technologies = []
-
-[savedsearch://ESCU - Common Ransomware Notes - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back.
-how_to_implement = You must be ingesting data that records file-system activity from your hosts to populate the Endpoint Filesystem data-model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1485"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = It's possible that a legitimate file could be created with the same name used by ransomware note files.
-providing_technologies = []
-
-[savedsearch://ESCU - Create Remote Thread into LSASS - Rule]
-type = detection
-asset_type = Windows
-confidence = medium
-explanation = Detect remote thread creation into LSASS consistent with credential dumping.
-how_to_implement = This search needs Sysmon Logs with a Sysmon configuration, which includes EventCode 8 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
-annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
-known_false_positives = Other tools can access LSASS for legitimate reasons and generate an event. In these cases, tweaking the search may help eliminate noise.
-providing_technologies = []
-
-[savedsearch://ESCU - Create Service In Suspicious File Path - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = This detection is to identify a creation of "user mode service" where the service file path is located in non-common service folder in windows.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.
-annotations = {"kill_chain_phases": ["Privilege Escalation"], "mitre_attack": ["T1569.001, T1569.002"]}
-known_false_positives = unknown
-providing_technologies = []
-
-[savedsearch://ESCU - Create local admin accounts using net exe - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for the creation of local administrator accounts using net.exe.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136.001"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Administrators often leverage net.exe to create admin accounts.
-providing_technologies = []
-
-[savedsearch://ESCU - Create or delete windows shares using net exe - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for the creation or deletion of hidden shares using net.exe.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070.005"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate.
-providing_technologies = []
-
-[savedsearch://ESCU - Creation of Shadow Copy - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy.
-how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"]}
-known_false_positives = Legitimate administrator usage of Vssadmin or Wmic will create false positives.
-providing_technologies = []
-
-[savedsearch://ESCU - Creation of Shadow Copy with wmic and powershell - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search detects the use of wmic and Powershell to create a shadow copy.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"]}
-known_false_positives = Legtimate administrator usage of wmic to create a shadow copy.
-providing_technologies = []
-
-[savedsearch://ESCU - Creation of lsass Dump with Taskmgr - Rule]
-type = detection
-asset_type = Windows
-confidence = medium
-explanation = Detect the hands on keyboard behavior of Windows Task Manager creating a prcoess dump of lsass.exe. Upon this behavior occurring, a file write/modification will occur in the users profile under \AppData\Local\Temp. The dump file, lsass.dmp, cannot be renamed, however if the dump occurs more than once, it will be named lsass (2).dmp.
-how_to_implement = This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 11 for detecting file create of lsass.dmp. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
-annotations = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
-known_false_positives = Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual.
-providing_technologies = []
-
-[savedsearch://ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search detects credential dumping using copy command from a shadow copy.
-how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"]}
-known_false_positives = unknown
-providing_technologies = []
-
-[savedsearch://ESCU - Credential Dumping via Symlink to Shadow Copy - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search detects the creation of a symlink to a shadow copy.
-how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"]}
-known_false_positives = unknown
-providing_technologies = []
-
-[savedsearch://ESCU - DNS Query Length Outliers - MLTK - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment.
-how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of DNS Query Length - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\
-This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\
-1. \
-1. **Label:** DNS Query Length, **Field:** query_length\
-1. \
-1. **Label:** Number of events, **Field:** count\
-Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`
-annotations = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.004"], "nist": ["PR.PT", "DE.AE", "DE.CM"]}
-known_false_positives = If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data.
-providing_technologies = []
-
-[savedsearch://ESCU - DNS Query Length With High Standard Deviation - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment.
-how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model.
-annotations = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048.003"], "nist": ["PR.PT", "DE.AE", "DE.CM"]}
-known_false_positives = It's possible there can be long domain names that are legitimate.
-providing_technologies = []
-
-[savedsearch://ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework.
-how_to_implement = To successfully implement this search you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that your DNS servers are identified correctly in the Assets and Identity table of Enterprise Security.
-annotations = {"cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.004"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]}
-known_false_positives = Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate.
-providing_technologies = []
-
-[savedsearch://ESCU - DNS record changed - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day.
-how_to_implement = To successfully implement this search you will need to ensure that DNS data is populating the `Network_Resolution` data model. It also requires that the `discover_dns_record` lookup table be populated by the included support search "Discover DNS record". \
- **Splunk>Phantom Playbook Integration**\
-If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment" can be configured to run when any results are found by this detection search. The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal to detect if DNS issuers changed. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \
-(Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\
-
-annotations = {"cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.004"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]}
-known_false_positives = Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate.
-providing_technologies = []
-
-[savedsearch://ESCU - Deleting Shadow Copies - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service. Wmic is an interface to the Windows Management Instrumentation. This search looks for either of these tools being used to delete shadow copies.
-how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1490"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
-known_false_positives = vssadmin.exe and wmic.exe are standard applications shipped with modern versions of windows. They may be used by administrators to legitimately delete old backup copies, although this is typically rare.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect API activity from users without MFA - Rule]
-type = detection
-asset_type = AWS Instance
-confidence = medium
-explanation = This search looks for CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Leverage the support search `Create a list of approved AWS service accounts`: run it once every 30 days to create a list of service accounts and validate them.\
-This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\
-1. \
-1. **Label:** AWS User ARN, **Field:** userIdentity.arn\
-1. \
-1. **Label:** AWS User Type, **Field:** userIdentity.type\
-Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`
-annotations = {"cis20": ["CIS 16"], "nist": ["DE.DP", "PR.AC"]}
-known_false_positives = Many service accounts configured within an AWS infrastructure do not have multi factor authentication enabled. Please ignore the service accounts, if triggered and instead add them to the aws_service_accounts.csv file to fine tune the detection. It is also possible that the search detects users in your environment using Single Sign-On systems, since the MFA is not handled by AWS.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect ARP Poisoning - Rule]
-type = detection
-asset_type = Infrastructure
-confidence = medium
-explanation = By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure.
-how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and Dynamic ARP Inspection (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-2_2_e/security/configuration_guide/b_sec_1522e_2960x_cg/b_sec_1522e_2960x_cg_chapter_01111.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices.
-annotations = {"cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557.002"], "nist": ["ID.AM", "PR.DS"]}
-known_false_positives = This search might be prone to high false positives if DHCP Snooping or ARP inspection has been incorrectly configured, or if a device normally sends many ARP packets (unlikely).
-providing_technologies = []
-
-[savedsearch://ESCU - Detect AWS API Activities From Unapproved Accounts - Rule]
-type = detection
-asset_type = AWS Instance
-confidence = medium
-explanation = This search looks for successful CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. Deprecated because managing this list can be quite hard.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also populate the `identity_lookup_expanded` lookup shipped with the Asset and Identity framework to be able to look up users in your identity table in Enterprise Security (ES). Leverage the support search called "Create a list of approved AWS service accounts": run it once every 30 days to create and validate a list of service accounts.\
-This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\
-1. \
-1. **Label:** First Time, **Field:** firstTime\
-1. \
-1. **Label:** Last Time, **Field:** lastTime\
-Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`
-annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.CM", "PR.AC", "ID.AM"]}
-known_false_positives = It's likely that you'll find activity detected by users/service accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` file. If the user is a legitimate service account, update the `aws_service_accounts.csv` table with that entry.
-providing_technologies = []
-
[savedsearch://ESCU - Detect AWS Console Login by New User - Rule]
type = detection
asset_type = AWS Instance
@@ -2093,264 +527,6 @@ annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives
known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.
providing_technologies = []
-[savedsearch://ESCU - Detect Activity Related to Pass the Hash Attacks - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique.
-how_to_implement = To successfully implement this search, you must ingest your Windows Security Event logs and leverage the latest TA for Windows.
-annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1550.002"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
-known_false_positives = Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Baron Samedit CVE-2021-3156 - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search detects the heap-based buffer overflow of sudoedit
-how_to_implement = Splunk Universal Forwarder running on Linux systems, capturing logs from the /var/log directory. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags.
-annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 16"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
-known_false_positives = unknown
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Baron Samedit CVE-2021-3156 Segfault - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search detects the heap-based buffer overflow of sudoedit
-how_to_implement = Splunk Universal Forwarder running on Linux systems (tested on Centos and Ubuntu), where segfaults are being logged. This also captures instances where the exploit has been compiled into a binary. The detection looks for greater than 5 instances of sudoedit combined with segfault over your search time period on a single host
-annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 16"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
-known_false_positives = If sudoedit is throwing segfaults for other reasons this will pick those up too.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Baron Samedit CVE-2021-3156 via OSQuery - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search detects the heap-based buffer overflow of sudoedit
-how_to_implement = OSQuery installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags.
-annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 16"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["DE.CM"]}
-known_false_positives = unknown
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Computer Changed with Anonymous Account - Rule]
-type = detection
-asset_type = Windows
-confidence = medium
-explanation = This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An account was successfully logged on) with an anonymous account.
-how_to_implement = This search requires audit computer account management to be enabled on the system in order to generate Event ID 4742. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Event Logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
-annotations = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1210"], "nist": ["DE.AE", "DE.CM"]}
-known_false_positives = None thus far found
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Credential Dumping through LSASS access - Rule]
-type = detection
-asset_type = Windows
-confidence = medium
-explanation = This search looks for reading lsass memory consistent with credential dumping.
-how_to_implement = This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
-annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["PR.IP", "PR.AC", "DE.CM"]}
-known_false_positives = The activity may be legitimate. Other tools can access lsass for legitimate reasons, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites.
-how_to_implement = You need to ingest data from your DNS logs in the Network_Resolution datamodel. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You will have to add legitimate domain names to the `legit_domains.csv` file shipped with the app. \
- **Splunk>Phantom Playbook Integration**\
-If Splunk>Phantom is also configured in your environment, a Playbook called `Lets Encrypt Domain Investigate` can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \
-(Playbook link:`https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/`).\
-
-annotations = {"cis20": ["CIS 8", "CIS 7"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1566.003"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]}
-known_false_positives = If a known good domain is not listed in the legit_domains.csv file, then the search could give you false postives. Please update that lookup file to filter out DNS requests to legitimate domains.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Excessive Account Lockouts From Endpoint - Rule]
-type = detection
-asset_type = Windows
-confidence = medium
-explanation = This search identifies endpoints that have caused a relatively high number of account lockouts in a short period.
-how_to_implement = You must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. \
- **Splunk>Phantom Playbook Integration**\
-If Splunk>Phantom is also configured in your environment, a Playbook called "Excessive Account Lockouts Enrichment and Response" can be configured to run when any results are found by this detection search. The Playbook executes the Contextual and Investigative searches in this Story, conducts additional information gathering on Windows endpoints, and takes a response action to shut down the affected endpoint. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \
-(Playbook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`).\
-
-annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078.002"], "nist": ["PR.IP"]}
-known_false_positives = It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Excessive User Account Lockouts - Rule]
-type = detection
-asset_type = Windows
-confidence = medium
-explanation = This search detects user accounts that have been locked out a relatively high number of times in a short period.
-how_to_implement = ou must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment.
-annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078.003"], "nist": ["PR.IP"]}
-known_false_positives = It is possible that a legitimate user is experiencing an issue causing multiple account login failures leading to lockouts.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Exchange Web Shell - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = The following query identifies suspicious .aspx created in 3 paths identified by Microsoft as known drop locations for Exchange exploitation related to HAFNIUM group. Paths include: `\HttpProxy\owa\auth\`, `\inetpub\wwwroot\aspnet_client\`, and `\HttpProxy\OAB\`. Upon triage, the suspicious .aspx file will likely look obvious on the surface. inspect the contents for script code inside. Identify additional log sources, IIS included, to review source and other potential exploitation.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` node.
-annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1505.003"]}
-known_false_positives = The query is structured in a way that `action` (read, create) is not defined. Review the results of this query, filter, and tune as necessary. It may be necessary to generate this query specific to your endpoint product.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect F5 TMUI RCE CVE-2020-5902 - Rule]
-type = detection
-asset_type = Network
-confidence = medium
-explanation = This search detects remote code exploit attempts on F5 BIG-IP, BIG-IQ, and Traffix SDC devices
-how_to_implement = To consistently detect exploit attempts on F5 devices using the vulnerabilities contained within CVE-2020-5902 it is recommended to ingest logs via syslog. As many BIG-IP devices will have SSL enabled on their management interfaces, detections via wire data may not pick anything up unless you are decrypting SSL traffic in order to inspect it. I am using a regex string from a Cloudflare mitigation technique to try and always catch the offending string (..;), along with the other exploit of using (hsqldb;).
-annotations = {"cis20": ["CIS 8", "CIS 11"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
-known_false_positives = unknown
-providing_technologies = []
-
-[savedsearch://ESCU - Detect GCP Storage access from a new IP - Rule]
-type = detection
-asset_type = GCP Storage Bucket
-confidence = medium
-explanation = This search looks at GCP Storage bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed a GCP Storage bucket.
-how_to_implement = This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). In order to capture public GCP Storage Bucket access logs, you must also enable storage bucket logging to your PubSub Topic as per https://cloud.google.com/storage/docs/access-logs. These logs are deposited into the nominated Storage Bucket on an hourly basis and typically show up by 15 minutes past the hour. It is recommended to configure any saved searches or correlation searches in Enterprise Security to run on an hourly basis at 30 minutes past the hour (cron definition of 30 * * * *). A lookup table (previously_seen_gcp_storage_access_from_remote_ip.csv) stores the previously seen access requests, and is used by this search to determine any newly seen IP addresses accessing the Storage Buckets.
-annotations = {"cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
-known_false_positives = GCP Storage buckets can be accessed from any IP (if the ACLs are open to allow it), as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past two hours.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect HTML Help Renamed - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed hh.exe may be used.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.001"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely a renamed instance of hh.exe will be used legitimately, filter as needed.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect HTML Help Spawn Child Process - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.001"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, some legitimate applications (ex. web browsers) may spawn a child process. Filter as needed.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect HTML Help URL in Command Line - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.001"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, some legitimate applications may retrieve a CHM remotely, filter as needed.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect HTML Help Using InfoTech Storage Handlers - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.001"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = It is rare to see instances of InfoTech Storage Handlers being used, but it does happen in some legitimate instances. Filter as needed.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect IPv6 Network Infrastructure Threats - Rule]
-type = detection
-asset_type = Infrastructure
-confidence = medium
-explanation = By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure.
-how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with one or more First Hop Security measures such as RA Guard, DHCP Guard and/or device tracking. See References for more information. The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices.
-annotations = {"cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557.002"], "nist": ["ID.AM", "PR.DS"]}
-known_false_positives = None currently known
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Large Outbound ICMP Packets - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity.
-how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model
-annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1095"], "nist": ["DE.AE"]}
-known_false_positives = ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with command and control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the macro `detect_large_outbound_icmp_packets_filter` to adjust the byte threshold or add specific IP addresses to an allow list.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Long DNS TXT Record Response - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. Deprecated because this detection should focus on DNS queries instead of DNS responses.
-how_to_implement = To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol.
-annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048.003"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]}
-known_false_positives = It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect MSHTA Url in Command Line - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = It is possible legitimate applications may perform this behavior and will need to be filtered.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Mimikatz Using Loaded Images - Rule]
-type = detection
-asset_type = Windows
-confidence = medium
-explanation = This search looks for reading loaded Images unique to credential dumping with Mimikatz. Deprecated because mimikatz libraries changed and very noisy sysmon Event Code.
-how_to_implement = This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 7 with powershell.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
-annotations = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.AE", "DE.CM"]}
-known_false_positives = Other tools can import the same DLLs. These tools should be part of a whitelist. False positives may be present with any process that authenticates or uses credentials, PowerShell included. Filter based on parent process.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Mimikatz Via PowerShell And EventCode 4703 - Rule]
-type = detection
-asset_type = Windows
-confidence = medium
-explanation = This search looks for PowerShell requesting privileges consistent with credential dumping. Deprecated, looks like things changed from a logging perspective.
-how_to_implement = You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes.
-annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["PR.IP", "PR.AC", "DE.CM"]}
-known_false_positives = The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect New Local Admin account - Rule]
-type = detection
-asset_type = Windows
-confidence = medium
-explanation = This search looks for newly created accounts that have been elevated to local administrators.
-how_to_implement = You must be ingesting Windows event logs using the Splunk Windows TA and collecting event code 4720 and 4732
-annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1136.001"], "nist": ["PR.AC", "DE.CM"]}
-known_false_positives = The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not "Administrators", this search may generate an excessive number of false positives
-providing_technologies = []
-
-[savedsearch://ESCU - Detect New Login Attempts to Routers - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days.
-how_to_implement = To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure.
-annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "PR.AC", "PR.IP"]}
-known_false_positives = Legitimate router connections may appear as new connections
-providing_technologies = []
-
-[savedsearch://ESCU - Detect New Open GCP Storage Buckets - Rule]
-type = detection
-asset_type = GCP Storage Bucket
-confidence = medium
-explanation = This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket.
-how_to_implement = This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview).
-annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
-known_false_positives = While this search has no known false positives, it is possible that a GCP admin has legitimately created a public bucket for a specific purpose. That said, GCP strongly advises against granting full control to the "allUsers" group.
-providing_technologies = []
-
[savedsearch://ESCU - Detect New Open S3 Buckets over AWS CLI - Rule]
type = detection
asset_type = S3 Bucket
@@ -2371,243 +547,6 @@ annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives
known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group.
providing_technologies = []
-[savedsearch://ESCU - Detect Oulook exe writing a zip file - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk.
-how_to_implement = You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon.
-annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1566.001"], "nist": ["ID.AM", "PR.DS"]}
-known_false_positives = It is not uncommon for outlook to write legitimate zip files to the disk.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Outbound SMB Traffic - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor.
-how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the companys assets in `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model
-annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1071.002"], "nist": ["DE.CM"]}
-known_false_positives = It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Path Interception By Creation Of program exe - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1574.009"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = unknown
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Port Security Violation - Rule]
-type = detection
-asset_type = Infrastructure
-confidence = medium
-explanation = By enabling Port Security on a Cisco switch you can restrict input to an interface by limiting and identifying MAC addresses of the workstations that are allowed to access the port. When you assign secure MAC addresses to a secure port, the port does not forward packets with source addresses outside the group of defined addresses. If you limit the number of secure MAC addresses to one and assign a single secure MAC address, the workstation attached to that port is assured the full bandwidth of the port. If a port is configured as a secure port and the maximum number of secure MAC addresses is reached, when the MAC address of a workstation attempting to access the port is different from any of the identified secure MAC addresses, a security violation occurs.
-how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with Port Security and Error Disable for this to work (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices.
-annotations = {"cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Exploitation", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557.002"], "nist": ["ID.AM", "PR.DS"]}
-known_false_positives = This search might be prone to high false positives if you have malfunctioning devices connected to your ethernet ports or if end users periodically connect physical devices to the network.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Prohibited Applications Spawning cmd exe - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe.
-how_to_implement = You must be ingesting data that records process activity from your hosts and populates the Endpoint data model with the resultant dataset. This search includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.003"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect PsExec With accepteula Flag - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.002"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Rare Executables - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process.
-how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts and populating the endpoint data model with the resultant dataset. The macro `filter_rare_process_allow_list` searches two lookup files for allowed processes. These consist of `rare_process_allow_list_default.csv` and `rare_process_allow_list_local.csv`. To add your own processes to the allow list, add them to `rare_process_allow_list_local.csv`. If you wish to remove an entry from the default lookup file, you will have to modify the macro itself to set the allow_list value for that process to false. You can modify the limit parameter and search scheduling to better suit your environment.
-annotations = {"cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.PT", "PR.DS", "DE.CM"]}
-known_false_positives = Some legitimate processes may be only rarely executed in your environment. As these are identified, update `rare_process_allow_list_local.csv` to filter them out of your search results.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Regasm Spawning a Process - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies regasm.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Regasm with Network Connection - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies regasm.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, limited instances of regasm.exe with a network connection may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Regasm with no Command Line Arguments - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies regasm.exe with no command line arguments. This particular behavior occurs when another process injects into regasm.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, limited instances of regasm.exe or may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Regsvcs Spawning a Process - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies regsvcs.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Regsvcs with Network Connection - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies Regsvcs.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Regsvcs with No Command Line Arguments - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies regsvcs.exe with no command line arguments. This particular behavior occurs when another process injects into regsvcs.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.009"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Regsvr32 Application Control Bypass - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \
-Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution.
-how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by modifying/removing the !=regsv32.exe.
-annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.010"], "nist": ["DE.CM"]}
-known_false_positives = Limited false positives related to third party software registering .DLL's.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Rogue DHCP Server - Rule]
-type = detection
-asset_type = Infrastructure
-confidence = medium
-explanation = By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack).
-how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping enabled (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices.
-annotations = {"cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1557"], "nist": ["ID.AM", "PR.DS"]}
-known_false_positives = This search might be prone to high false positives if DHCP Snooping has been incorrectly configured or in the unlikely event that the DHCP server has been moved to another network interface.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Rundll32 Application Control Bypass - advpack - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, some legitimate applications may use advpack.dll or ieadvpack.dll, triggering a false positive.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Rundll32 Application Control Bypass - setupapi - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, some legitimate applications may use setupapi triggering a false positive.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Rundll32 Application Control Bypass - syssetup - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, some legitimate applications may use syssetup.dll, triggering a false positive.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Rundll32 Inline HTA Execution - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "rundll32.exe" and its parent process.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect S3 access from a new IP - Rule]
-type = detection
-asset_type = S3 Bucket
-confidence = medium
-explanation = This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names.
-annotations = {"cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
-known_false_positives = S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour
-providing_technologies = []
-
-[savedsearch://ESCU - Detect SNICat SNI Exfiltration - Rule]
-type = detection
-asset_type = Network
-confidence = medium
-explanation = This search looks for commands that the SNICat tool uses in the TLS SNI field.
-how_to_implement = You must be ingesting Zeek SSL data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when any of the predefined SNICat commands are found within the server_name (SNI) field. These commands are LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito. You can go further once this has been detected, and run other searches to decode the SNI data to prove or disprove if any data exfiltration has taken place.
-annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1041"], "nist": ["PR.DS", "DE.CM", "DE.AE"]}
-known_false_positives = Unknown
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Software Download To Network Device - Rule]
-type = detection
-asset_type = Infrastructure
-confidence = medium
-explanation = Adversaries may abuse netbooting to load an unauthorized network device operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot (netbooting) is commonly used by network administrators to load configuration-controlled network device images from a centralized management server. Netbooting is one option in the boot sequence and can be used to centralize, manage, and control device images.
-how_to_implement = This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from network devices. Make sure to tag any network devices as network, router or switch in order for this detection to work. If the TFTP traffic doesn't traverse a firewall nor packet inspection, these events will not be logged. This is typically an issue if the TFTP server is on the same subnet as the network device. There is also a chance of the network device loading software using a DHCP assigned IP address (netboot) which is not in the Asset inventory.
-annotations = {"cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1542.005"], "nist": ["ID.AM", "PR.DS"]}
-known_false_positives = This search will also report any legitimate attempts of software downloads to network devices as well as outbound SSH sessions from network devices.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Spike in AWS API Activity - Rule]
-type = detection
-asset_type = AWS Instance
-confidence = medium
-explanation = This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.\
-This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\
-1. \
-1. **Label:** Number of API Calls, **Field:** numberOfApiCalls\
-1. \
-1. **Label:** Unique API Calls, **Field:** uniqueApisCalled\
-Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`
-annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.CM", "PR.AC"]}
-known_false_positives =
-providing_technologies = []
-
[savedsearch://ESCU - Detect Spike in AWS Security Hub Alerts for EC2 Instance - Rule]
type = detection
asset_type = AWS Instance
@@ -2618,964 +557,6 @@ annotations = {"cis20": ["CIS 13"], "nist": ["DE.DP", "DE.AE"]}
known_false_positives = None
providing_technologies = []
-[savedsearch://ESCU - Detect Spike in AWS Security Hub Alerts for User - Rule]
-type = detection
-asset_type = AWS Instance
-confidence = medium
-explanation = This search looks for a spike in number of of AWS security Hub alerts for an AWS IAM User in 4 hours intervals.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval.
-annotations = {"cis20": ["CIS 13"], "nist": ["DE.DP", "DE.AE"]}
-known_false_positives = None
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Spike in Network ACL Activity - Rule]
-type = detection
-asset_type = AWS Instance
-confidence = medium
-explanation = This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. This search is deprecated and have been translated to use the latest Change Datamodel.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Network ACL Activity by ARN" support search once to create a lookup file of previously seen Network ACL Activity. To add or remove API event names related to network ACLs, edit the macro `network_acl_events`.
-annotations = {"cis20": ["CIS 12", "CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.007"], "nist": ["DE.DP", "DE.CM", "PR.AC"]}
-known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Please modify this according the your environment.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Spike in S3 Bucket deletion - Rule]
-type = detection
-asset_type = S3 Bucket
-confidence = medium
-explanation = This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity.
-annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1530"], "nist": ["DE.DP", "DE.CM", "PR.AC"]}
-known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Spike in Security Group Activity - Rule]
-type = detection
-asset_type = AWS Instance
-confidence = medium
-explanation = This search will detect users creating spikes in API activity related to security groups in your AWS environment. It will also update the cache file that factors in the latest data. This search is deprecated and have been translated to use the latest Change Datamodel.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.This search works best when you run the "Baseline of Security Group Activity by ARN" support search once to create a history of previously seen Security Group Activity. To add or remove API event names for security groups, edit the macro `security_group_api_calls`.
-annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.CM", "PR.AC"]}
-known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule]
-type = detection
-asset_type = AWS Instance
-confidence = medium
-explanation = This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections.
-annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "nist": ["DE.AE", "DE.CM", "PR.AC"]}
-known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Traffic Mirroring - Rule]
-type = detection
-asset_type = Infrastructure
-confidence = medium
-explanation = Adversaries may leverage traffic mirroring in order to automate data exfiltration over compromised network infrastructure. Traffic mirroring is a native feature for some network devices and used for network analysis and may be configured to duplicate traffic and forward to one or more destinations for analysis by a network analyzer or other monitoring device.
-how_to_implement = This search uses a standard SPL query on logs from Cisco Network devices. The network devices must log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices and that the devices have been configured according to the documentation of the Cisco Networks Add-on. Also note that an attacker may disable logging from the device prior to enabling traffic mirroring.
-annotations = {"cis20": ["CIS 1", "CIS 11"], "kill_chain_phases": ["Delivery", "Actions on Objectives"], "mitre_attack": ["T1200", "T1498", "T1020.001"], "nist": ["ID.AM", "PR.DS"]}
-known_false_positives = This search will return false positives for any legitimate traffic captures by network administrators.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect USB device insertion - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework.
-how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663 and 4656. Ensure that the field from the event logs is being mapped to the result_id field in the Change_Analysis data model. To minimize the alert volume, this search leverages the Assets and Identity framework to filter out events from those assets not marked high priority in the Enterprise Security Assets and Identity Framework.
-annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "nist": ["PR.PT", "PR.DS"]}
-known_false_positives = Legitimate USB activity will also be detected. Please verify and investigate as appropriate.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Unauthorized Assets by MAC address - Rule]
-type = detection
-asset_type = Infrastructure
-confidence = medium
-explanation = By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found.
-how_to_implement = This search uses the Network_Sessions data model shipped with Enterprise Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv file located in SA-IdentityManagement, which will contain a list of known authorized organizational assets including their MAC addresses. Ensure that all inventoried systems have their MAC address populated.
-annotations = {"cis20": ["CIS 1"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]}
-known_false_positives = This search might be prone to high false positives. Please consider this when conducting analysis or investigations. Authorized devices may be detected as unauthorized. If this is the case, verify the MAC address of the system responsible for the false positive and add it to the Assets and Identity framework with the proper information.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine
-how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.003"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Some legitimate applications may exhibit this behavior.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Windows DNS SIGRed via Splunk Stream - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search detects SIGRed via Splunk Stream.
-how_to_implement = You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size via stream:tcp. Replace the macro definitions ('stream:dns' and 'stream:tcp') with configurations for your Splunk environment.
-annotations = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1203"], "nist": ["DE.CM"]}
-known_false_positives = unknown
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Windows DNS SIGRed via Zeek - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search detects SIGRed via Zeek DNS and Zeek Conn data.
-how_to_implement = You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting SIG and KEY records via bro:dns:json and TCP payload over 65KB in size via bro:conn:json. The Network Resolution and Network Traffic datamodels are in use for this search.
-annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1203"], "nist": ["DE.CM"]}
-known_false_positives = unknown
-providing_technologies = []
-
-[savedsearch://ESCU - Detect Zerologon via Zeek - Rule]
-type = detection
-asset_type = Network
-confidence = medium
-explanation = This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vulnerability via Zeek RPC
-how_to_implement = You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should also be getting ingested in JSON format. We are detecting when all three RPC operations (NetrServerReqChallenge, NetrServerAuthenticate3, NetrServerPasswordSet2) are splunk_security_essentials_app via bro:rpc:json. These three operations are then correlated on the Zeek UID field.
-annotations = {"cis20": ["CIS 8", "CIS 11"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"], "nist": ["DE.CM"]}
-known_false_positives = unknown
-providing_technologies = []
-
-[savedsearch://ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule]
-type = detection
-asset_type = Web Server
-confidence = medium
-explanation = This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity.
-how_to_implement = You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model.
-annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1082"]}
-known_false_positives = It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect hosts connecting to dynamic domain providers - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, block lists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains.
-how_to_implement = First, you'll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is being queried and the IP of the host originating the request must be populating the `Network_Resolution` data model. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating the local lookup periodically by adding new domains to the list of `dynamic_dns_providers_local.csv`.\
-This search produces fields (query, answer, isDynDNS) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable event. To see the additional metadata, add the following fields, if not already present, to Incident Review. Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\
-1. \
-1. **Label:** DNS Answer, **Field:** answer\
-1. \
-1. **Label:** IsDynamicDNS, **Field:** isDynDNS\
-Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`
-annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1189"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]}
-known_false_positives = Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect malicious requests to exploit JBoss servers - Rule]
-type = detection
-asset_type = Web Server
-confidence = medium
-explanation = This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL.
-how_to_implement = You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model
-annotations = {"cis20": ["CIS 12", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]}
-known_false_positives = No known false positives for this detection.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect mshta inline hta execution - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect mshta renamed - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies renamed instances of mshta.exe executing. Mshta.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. This analytic utilizes the internal name of the PE to identify if is the legitimate mshta binary. Further analysis should be performed to review the executed content and validation it is the real mshta.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, some legitimate applications may use a moved copy of mshta.exe, but never renamed, triggering a false positive.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect new API calls from user roles - Rule]
-type = detection
-asset_type = AWS Instance
-confidence = medium
-explanation = This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in CloudTrail" support search once to create a history of previously seen user roles.
-annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]}
-known_false_positives = It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect new user AWS Console Login - Rule]
-type = detection
-asset_type = AWS Instance
-confidence = medium
-explanation = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour. Deprecated now this search is updated to use the Authentication datamodel.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines.
-annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078.004"], "nist": ["DE.DP", "DE.AE"]}
-known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect processes used for System Network Configuration Discovery - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for fast execution of processes used for system network configuration discovery on the endpoint.
-how_to_implement = You must be ingesting data that records registry activity from your hosts to populate the Endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report reads and writes to the registry or that are populated via Windows event logs, after enabling process tracking in your Windows audit settings.
-annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "mitre_attack": ["T1016"], "nist": ["ID.AM", "PR.DS"]}
-known_false_positives = It is uncommon for normal users to execute a series of commands used for network discovery. System administrators often use scripts to execute these commands. These can generate false positives.
-providing_technologies = []
-
-[savedsearch://ESCU - Detect web traffic to dynamic domain providers - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for web connections to dynamic DNS providers.
-how_to_implement = This search requires you to be ingesting web-traffic logs. You can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis tool, such as Bro or Splunk Stream. The web data model must contain the URL being requested, the IP address of the host initiating the request, and the destination IP. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of dynamic DNS providers. Consider periodically updating this local lookup file with new domains.\
-This search produces fields (`isDynDNS`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** IsDynamicDNS, **Field:** isDynDNS\
-Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` Deprecated because duplicate.
-annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1071.001"], "nist": ["PR.IP", "DE.DP"]}
-known_false_positives = It is possible that list of dynamic DNS providers is outdated and/or that the URL being requested is legitimate.
-providing_technologies = []
-
-[savedsearch://ESCU - Detection of DNS Tunnels - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. Deprecated because existing detection is doing the same.
-how_to_implement = To successfully implement this search, we must ensure that DNS data is being ingested and mapped to the appropriate fields in the Network_Resolution data model. Fields like src_category are automatically provided by the Assets and Identity Framework shipped with Splunk Enterprise Security. You will need to ensure you are using the Assets and Identity Framework and populating the src_category field. You will also need to enable the `cim_corporate_web_domain_search()` macro which will essentially filter out the DNS queries made to the corporate web domains to reduce alert fatigue.
-annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1048.003"], "nist": ["PR.PT", "PR.DS"]}
-known_false_positives = It's possible that normal DNS traffic will exhibit this behavior. If an alert is generated, please investigate and validate as appropriate. The threshold can also be modified to better suit your environment.
-providing_technologies = []
-
-[savedsearch://ESCU - Detection of tools built by NirSoft - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers.
-how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1072"], "nist": ["PR.IP"]}
-known_false_positives = While legitimate, these NirSoft tools are prone to abuse. You should verfiy that the tool was used for a legitimate purpose.
-providing_technologies = []
-
-[savedsearch://ESCU - Disabling Remote User Account Control - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC).
-how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1548.002"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = This registry key may be modified via administrators to implement a change in system policy. This type of change should be a very rare occurrence.
-providing_technologies = []
-
-[savedsearch://ESCU - Dump LSASS via comsvcs DLL - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = Detect the usage of comsvcs.dll for dumping the lsass process.
-how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
-known_false_positives = None identified.
-providing_technologies = []
-
-[savedsearch://ESCU - Dump LSASS via procdump - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = Detect procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. This query does not monitor for the internal name (OriginalFileName=procdump) of the PE or look for procdump64.exe. Modify the query as needed.\
-During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
-known_false_positives = None identified.
-providing_technologies = []
-
-[savedsearch://ESCU - Dump LSASS via procdump Rename - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = Detect a renamed instance of procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. Modify the query as needed.\
-During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
-known_false_positives = None identified.
-providing_technologies = []
-
-[savedsearch://ESCU - EC2 Instance Modified With Previously Unseen User - Rule]
-type = detection
-asset_type = AWS Instance
-confidence = medium
-explanation = This search looks for EC2 instances being modified by users who have not previously modified them. This search is deprecated and have been translated to use the latest Change Datamodel.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`.
-annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]}
-known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior.
-providing_technologies = []
-
-[savedsearch://ESCU - EC2 Instance Started In Previously Unseen Region - Rule]
-type = detection
-asset_type = AWS Instance
-confidence = medium
-explanation = This search looks for CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen AWS Regions" support search only once to create of baseline of previously seen regions. This search is deprecated and have been translated to use the latest Change Datamodel.
-annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1535"], "nist": ["DE.DP", "DE.AE"]}
-known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate.
-providing_technologies = []
-
-[savedsearch://ESCU - EC2 Instance Started With Previously Unseen AMI - Rule]
-type = detection
-asset_type = AWS Instance
-confidence = medium
-explanation = This search looks for EC2 instances being created with previously unseen AMIs. This search is deprecated and have been translated to use the latest Change Datamodel.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 AMIs" support search once to create a history of previously seen AMIs.
-annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]}
-known_false_positives = After a new AMI is created, the first systems created with that AMI will cause this alert to fire. Verify that the AMI being used was created by a legitimate user.
-providing_technologies = []
-
-[savedsearch://ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule]
-type = detection
-asset_type = AWS Instance
-confidence = medium
-explanation = This search looks for EC2 instances being created with previously unseen instance types. This search is deprecated and have been translated to use the latest Change Datamodel.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types.
-annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]}
-known_false_positives = It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type.
-providing_technologies = []
-
-[savedsearch://ESCU - EC2 Instance Started With Previously Unseen User - Rule]
-type = detection
-asset_type = AWS Instance
-confidence = medium
-explanation = This search looks for EC2 instances being created by users who have not created them before. This search is deprecated and have been translated to use the latest Change Datamodel.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs.
-annotations = {"cis20": ["CIS 1"], "mitre_attack": ["T1078.004"], "nist": ["ID.AM"]}
-known_false_positives = It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior.
-providing_technologies = []
-
-[savedsearch://ESCU - Email Attachments With Lots Of Spaces - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names.
-how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. \
- **Splunk Phantom Playbook Integration**\
-If Splunk Phantom is also configured in your environment, a playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox.
-annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]}
-known_false_positives = None at this time
-providing_technologies = []
-
-[savedsearch://ESCU - Email files written outside of the Outlook directory - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory.
-how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.001"]}
-known_false_positives = Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search.
-providing_technologies = []
-
-[savedsearch://ESCU - Email servers sending high volume traffic to hosts - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server.
-how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid.
-annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.002"], "nist": ["PR.PT", "DE.CM", "DE.AE"]}
-known_false_positives = The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers.
-providing_technologies = []
-
-[savedsearch://ESCU - Eventvwr UAC Bypass - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = The following search identifies Eventvwr bypass by identifying the registry modification into a specific path that eventvwr.msc looks to (but is not valid) upon execution. A successful attack will include a suspicious command to be executed upon eventvwr.msc loading. Upon triage, review the parallel processes that have executed. Identify any additional registry modifications on the endpoint that may look suspicious. Remediate as necessary.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-annotations = {"kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1548.002"]}
-known_false_positives = Some false positives may be present and will need to be filtered.
-providing_technologies = []
-
-[savedsearch://ESCU - Excessive DNS Failures - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences.
-how_to_implement = To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model.
-annotations = {"cis20": ["CIS 8", "CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.004"], "nist": ["PR.PT", "DE.AE", "DE.CM"]}
-known_false_positives = It is possible legitimate traffic can trigger this rule. Please investigate as appropriate. The threshold for generating an event can also be customized to better suit your environment.
-providing_technologies = []
-
-[savedsearch://ESCU - Execution of File With Spaces Before Extension - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view.
-how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036.003"], "nist": ["DE.CM", "PR.PT", "PR.IP"]}
-known_false_positives = None identified.
-providing_technologies = []
-
-[savedsearch://ESCU - Execution of File with Multiple Extensions - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content.
-how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node.
-annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036.003"], "nist": ["DE.CM", "PR.PT", "PR.IP"]}
-known_false_positives = None identified.
-providing_technologies = []
-
-[savedsearch://ESCU - Extended Period Without Successful Netbackup Backups - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search returns a list of hosts that have not successfully completed a backup in over a week. Deprecated because it's a infrastructure monitoring.
-how_to_implement = To successfully implement this search you need to first obtain data from your backup solution, either from the backup logs on your hosts, or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your backup solution. Depending on how often you backup your systems, you may want to modify how far in the past to look for a successful backup, other than the default of seven days.
-annotations = {"cis20": ["CIS 10"], "nist": ["PR.IP"]}
-known_false_positives = None identified
-providing_technologies = []
-
-[savedsearch://ESCU - File with Samsam Extension - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The search looks for file writes with extensions consistent with a SamSam ransomware attack.
-how_to_implement = You must be ingesting data that records file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Because these extensions are not typically used in normal operations, you should investigate all results.
-providing_technologies = []
-
-[savedsearch://ESCU - First Time Seen Child Process of Zoom - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You should run the baseline search `Previously Seen Zoom Child Processes - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Zoom Child Processes - Update` to keep this table up to date and to age out old child processes. Please update the `previously_seen_zoom_child_processes_window` macro to adjust the time window.
-annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1068"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
-known_false_positives = A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken.
-providing_technologies = []
-
-[savedsearch://ESCU - First Time Seen Running Windows Service - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for the first and last time a Windows service is seen running in your environment. This table is then cached.
-how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows system event logs in order for this search to execute successfully. You should run the baseline search `Previously Seen Running Windows Services - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Running Windows Services - Update` to keep this table up to date and to age out old Windows Services. Please update the `previously_seen_windows_services_window` macro to adjust the time window. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above.
-annotations = {"cis20": ["CIS 2", "CIS 9"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1569.002"], "nist": ["ID.AM", "PR.DS", "PR.AC", "DE.AE"]}
-known_false_positives = A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process.
-providing_technologies = []
-
-[savedsearch://ESCU - First time seen command line argument - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. Please make sure you run the support search "Previously seen command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the `relative_time` evaluation function.
-annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001", "T1059.003"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
-known_false_positives = Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name
-providing_technologies = []
-
-[savedsearch://ESCU - FodHelper UAC Bypass - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = Fodhelper.exe has a known UAC bypass as it attempts to look for specific registry keys upon execution, that do not exist. Therefore, an attacker can write its malicious commands in these registry keys to be executed by fodhelper.exe with the highest privilege. \
-1. `HKCU:\Software\Classes\ms-settings\shell\open\command`\
-1. `HKCU:\Software\Classes\ms-settings\shell\open\command\DelegateExecute`\
-1. `HKCU:\Software\Classes\ms-settings\shell\open\command\(default)`\
-Upon triage, fodhelper.exe will have a child process and read access will occur on the registry keys. Isolate the endpoint and review parallel processes for additional behavior.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1112", "T1548.002"]}
-known_false_positives = Limited to no false positives are expected.
-providing_technologies = []
-
-[savedsearch://ESCU - GCP Detect accounts with high risk roles by project - Rule]
-type = detection
-asset_type = GCP Account
-confidence = medium
-explanation = This search provides detection of accounts with high risk roles by projects. Compromised accounts with high risk roles can move laterally or even scalate privileges at different projects depending on organization schema.
-how_to_implement = You must install splunk GCP add-on. This search works with gcp:pubsub:message logs
-annotations = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
-known_false_positives = Accounts with high risk roles should be reduced to the minimum number needed, however specific tasks and setups may be simply expected behavior within organization
-providing_technologies = []
-
-[savedsearch://ESCU - GCP Detect gcploit framework - Rule]
-type = detection
-asset_type = GCP Account
-confidence = medium
-explanation = This search provides detection of GCPloit exploitation framework. This framework can be used to escalate privileges and move laterally from compromised high privilege accounts.
-how_to_implement = You must install splunk GCP add-on. This search works with gcp:pubsub:message logs
-annotations = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
-known_false_positives = Payload.request.function.timeout value can possibly be match with other functions or requests however the source user and target request account may indicate an attempt to move laterally accross acounts or projects
-providing_technologies = []
-
-[savedsearch://ESCU - GCP Detect high risk permissions by resource and account - Rule]
-type = detection
-asset_type = GCP Account
-confidence = medium
-explanation = This search provides detection of high risk permissions by resource and accounts. These are permissions that can allow attackers with compromised accounts to move laterally and escalate privileges.
-how_to_implement = You must install splunk GCP add-on. This search works with gcp:pubsub:message logs
-annotations = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
-known_false_positives = High risk permissions are part of any GCP environment, however it is important to track resource and accounts usage, this search may produce false positives.
-providing_technologies = []
-
-[savedsearch://ESCU - GCP GCR container uploaded - Rule]
-type = detection
-asset_type = GCP GCR Container
-confidence = medium
-explanation = This search show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path.
-how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a subpub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_gcp_detection_filter` macro to filter out the false positives.
-annotations = {"mitre_attack": ["T1525"]}
-known_false_positives = Uploading container is a normal behavior from developers or users with access to container registry. GCP GCR registers container upload as a Storage event, this search must be considered under the context of CONTAINER upload creation which automatically generates a bucket entry for destination path.
-providing_technologies = []
-
-[savedsearch://ESCU - GCP Kubernetes cluster pod scan detection - Rule]
-type = detection
-asset_type = GCP Kubernetes cluster
-confidence = medium
-explanation = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods
-how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk.
-annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]}
-known_false_positives = Not all unauthenticated requests are malicious, but frequency, User Agent, source IPs and pods will provide context.
-providing_technologies = []
-
-[savedsearch://ESCU - GCP Kubernetes cluster scan detection - Rule]
-type = detection
-asset_type = GCP Kubernetes cluster
-confidence = medium
-explanation = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster
-how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs.
-annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]}
-known_false_positives = Not all unauthenticated requests are malicious, but frequency, User Agent and source IPs will provide context.
-providing_technologies = []
-
-[savedsearch://ESCU - Hiding Files And Directories With Attrib exe - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1222.001"], "nist": ["DE.CM"]}
-known_false_positives = Some applications and users may legitimately use attrib.exe to interact with the files.
-providing_technologies = []
-
-[savedsearch://ESCU - High File Deletion Frequency - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = This search looks for high frequency of file deletion relative to process name and process id. These events usually happen when the ransomware tries to encrypt the files with the ransomware file extensions and sysmon treat the original files to be deleted as soon it was replace as encrypted data.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the deleted target file name, process name and process id from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1485"]}
-known_false_positives = user may delete bunch of pictures or files in a folder.
-providing_technologies = []
-
-[savedsearch://ESCU - High Number of Login Failures from a single source - Rule]
-type = detection
-asset_type = Office 365
-confidence = medium
-explanation = This search will detect more than 5 login failures in Office365 Azure Active Directory from a single source IP address. Please adjust the threshold value of 5 as suited for your environment.
-how_to_implement =
-annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1110.001"], "nist": ["DE.DP", "DE.AE"]}
-known_false_positives = unknown
-providing_technologies = []
-
-[savedsearch://ESCU - High Process Termination Frequency - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = This analytics are designed to indentify a high frequency of process termination on a machine which is a common behavior of ransomware malware before encrypting files. This technique is designed to avoid an exception error while accessing (docs, images, database and etc..) in the infected machine for encryption.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the Image (process full path of terminated process) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1486"]}
-known_false_positives = admin or user tool that can terminate multiple process.
-providing_technologies = []
-
-[savedsearch://ESCU - Hosts receiving high volume of network traffic from email server - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server.
-how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid.
-annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114.002"], "nist": ["PR.PT", "DE.CM", "DE.AE"]}
-known_false_positives = The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers.
-providing_technologies = []
-
-[savedsearch://ESCU - Identify New User Accounts - Rule]
-type = detection
-asset_type = Domain Server
-confidence = medium
-explanation = This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week.
-how_to_implement = To successfully implement this search, you need to be populating the Enterprise Security Identity_Management data model in the assets and identity framework.
-annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078.002"], "nist": ["PR.IP"]}
-known_false_positives = If the Identity_Management data model is not updated regularly, this search could give you false positive alerts. Please consider this and investigate appropriately.
-providing_technologies = []
-
-[savedsearch://ESCU - Kerberoasting spn request with RC4 encryption - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search detects a potential kerberoasting attack via service principal name requests
-how_to_implement = You must be ingesting endpoint data that tracks process activity, and include the windows security event logs that contain kerberos
-annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1558.003"], "nist": ["DE.CM"]}
-known_false_positives = Older systems that support kerberos RC4 by default NetApp may generate false positives
-providing_technologies = []
-
-[savedsearch://ESCU - Kubernetes AWS detect RBAC authorization by account - Rule]
-type = detection
-asset_type = AWS EKS Kubernetes cluster
-confidence = medium
-explanation = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences
-how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs
-annotations = {"kill_chain_phases": ["Lateral Movement"]}
-known_false_positives = Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted.
-providing_technologies = []
-
-[savedsearch://ESCU - Kubernetes AWS detect most active service accounts by pod - Rule]
-type = detection
-asset_type = AWS EKS Kubernetes cluster
-confidence = medium
-explanation = This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision
-how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs
-annotations = {"kill_chain_phases": ["Lateral Movement"]}
-known_false_positives = Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness.
-providing_technologies = []
-
-[savedsearch://ESCU - Kubernetes AWS detect sensitive role access - Rule]
-type = detection
-asset_type = AWS EKS Kubernetes cluster
-confidence = medium
-explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets
-how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs.
-annotations = {"kill_chain_phases": ["Lateral Movement"]}
-known_false_positives = Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use.
-providing_technologies = []
-
-[savedsearch://ESCU - Kubernetes AWS detect service accounts forbidden failure access - Rule]
-type = detection
-asset_type = AWS EKS Kubernetes cluster
-confidence = medium
-explanation = This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI
-how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs.
-annotations = {"kill_chain_phases": ["Lateral Movement"]}
-known_false_positives = This search can give false positives as there might be inherent issues with authentications and permissions at cluster.
-providing_technologies = []
-
-[savedsearch://ESCU - Kubernetes AWS detect suspicious kubectl calls - Rule]
-type = detection
-asset_type = AWS EKS Kubernetes cluster
-confidence = medium
-explanation = This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context
-how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs.
-annotations = {"kill_chain_phases": ["Lateral Movement"]}
-known_false_positives = Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets
-providing_technologies = []
-
-[savedsearch://ESCU - Kubernetes Azure detect RBAC authorization by account - Rule]
-type = detection
-asset_type = Azure AKS Kubernetes cluster
-confidence = medium
-explanation = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences
-how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
-annotations = {"kill_chain_phases": ["Lateral Movement"]}
-known_false_positives = Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted.
-providing_technologies = []
-
-[savedsearch://ESCU - Kubernetes Azure detect most active service accounts by pod namespace - Rule]
-type = detection
-asset_type = Azure AKS Kubernetes cluster
-confidence = medium
-explanation = This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb
-how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
-annotations = {"kill_chain_phases": ["Lateral Movement"]}
-known_false_positives = Not all service accounts interactions are malicious. Analyst must consider IP and verb context when trying to detect maliciousness.
-providing_technologies = []
-
-[savedsearch://ESCU - Kubernetes Azure detect sensitive object access - Rule]
-type = detection
-asset_type = Azure AKS Kubernetes cluster
-confidence = medium
-explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets
-how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
-annotations = {"kill_chain_phases": ["Lateral Movement"]}
-known_false_positives = Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection.
-providing_technologies = []
-
-[savedsearch://ESCU - Kubernetes Azure detect sensitive role access - Rule]
-type = detection
-asset_type = Azure AKS Kubernetes cluster
-confidence = medium
-explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets
-how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
-annotations = {"kill_chain_phases": ["Lateral Movement"]}
-known_false_positives = Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use.
-providing_technologies = []
-
-[savedsearch://ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule]
-type = detection
-asset_type = Azure AKS Kubernetes cluster
-confidence = medium
-explanation = This search provides information on Kubernetes service accounts with failure or forbidden access status
-how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
-annotations = {"kill_chain_phases": ["Lateral Movement"]}
-known_false_positives = This search can give false positives as there might be inherent issues with authentications and permissions at cluster.
-providing_technologies = []
-
-[savedsearch://ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule]
-type = detection
-asset_type = Azure AKS Kubernetes cluster
-confidence = medium
-explanation = This search provides information on rare Kubectl calls with IP, verb namespace and object access context
-how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
-annotations = {"kill_chain_phases": ["Lateral Movement"]}
-known_false_positives = Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially suspicious IPs and sensitive objects such as configmaps or secrets
-providing_technologies = []
-
-[savedsearch://ESCU - Kubernetes Azure pod scan fingerprint - Rule]
-type = detection
-asset_type = Azure AKS Kubernetes cluster
-confidence = medium
-explanation = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure
-how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
-annotations = {"kill_chain_phases": ["Reconnaissance"]}
-known_false_positives = Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context.
-providing_technologies = []
-
-[savedsearch://ESCU - Kubernetes Azure scan fingerprint - Rule]
-type = detection
-asset_type = Azure AKS Kubernetes cluster
-confidence = medium
-explanation = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure
-how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics
-annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1526"]}
-known_false_positives = Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context.
-providing_technologies = []
-
-[savedsearch://ESCU - Kubernetes GCP detect RBAC authorizations by account - Rule]
-type = detection
-asset_type = GCP GKE Kubernetes cluster
-confidence = medium
-explanation = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding top to see both extremes of RBAC by accounts occurrences
-how_to_implement = You must install splunk AWS add on for GCP. This search works with pubsub messaging service logs
-annotations = {"kill_chain_phases": ["Lateral Movement"]}
-known_false_positives = Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted.
-providing_technologies = []
-
-[savedsearch://ESCU - Kubernetes GCP detect most active service accounts by pod - Rule]
-type = detection
-asset_type = GCP GKE Kubernetes cluster
-confidence = medium
-explanation = This search provides information on Kubernetes service accounts,accessing pods by IP address, verb and decision
-how_to_implement = You must install splunk GCP add on. This search works with pubsub messaging service logs
-annotations = {"kill_chain_phases": ["Lateral Movement"]}
-known_false_positives = Not all service accounts interactions are malicious. Analyst must consider IP, verb and decision context when trying to detect maliciousness.
-providing_technologies = []
-
-[savedsearch://ESCU - Kubernetes GCP detect sensitive object access - Rule]
-type = detection
-asset_type = GCP GKE Kubernetes cluster
-confidence = medium
-explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmaps or secrets
-how_to_implement = You must install splunk add on for GCP . This search works with pubsub messaging service logs.
-annotations = {"kill_chain_phases": ["Lateral Movement"]}
-known_false_positives = Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection.
-providing_technologies = []
-
-[savedsearch://ESCU - Kubernetes GCP detect sensitive role access - Rule]
-type = detection
-asset_type = GCP GKE EKS Kubernetes cluster
-confidence = medium
-explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets
-how_to_implement = You must install splunk add on for GCP. This search works with pubsub messaging servicelogs.
-annotations = {"kill_chain_phases": ["Lateral Movement"]}
-known_false_positives = Sensitive role resource access is necessary for cluster operation, however source IP, user agent, decision and reason may indicate possible malicious use.
-providing_technologies = []
-
-[savedsearch://ESCU - Kubernetes GCP detect service accounts forbidden failure access - Rule]
-type = detection
-asset_type = GCP GKE Kubernetes cluster
-confidence = medium
-explanation = This search provides information on Kubernetes service accounts with failure or forbidden access status, this search can be extended by using top or rare operators to find trends or rarities in failure status, user agents, source IPs and request URI
-how_to_implement = You must install splunk add on for GCP. This search works with pubsub messaging service logs.
-annotations = {"kill_chain_phases": ["Lateral Movement"]}
-known_false_positives = This search can give false positives as there might be inherent issues with authentications and permissions at cluster.
-providing_technologies = []
-
-[savedsearch://ESCU - Kubernetes GCP detect suspicious kubectl calls - Rule]
-type = detection
-asset_type = GCP GKE Kubernetes cluster
-confidence = medium
-explanation = This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context
-how_to_implement = You must install splunk add on for GCP. This search works with pubsub messaging logs.
-annotations = {"kill_chain_phases": ["Lateral Movement"]}
-known_false_positives = Kubectl calls are not malicious by nature. However source IP, source user, user agent, object path, and authorization context can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets
-providing_technologies = []
-
-[savedsearch://ESCU - Large Volume of DNS ANY Queries - Rule]
-type = detection
-asset_type = DNS Servers
-confidence = medium
-explanation = The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries.
-how_to_implement = To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model.
-annotations = {"cis20": ["CIS 11", "CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1498.002"], "nist": ["PR.PT", "DE.AE", "PR.IP"]}
-known_false_positives = Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment.
-providing_technologies = []
-
-[savedsearch://ESCU - MacOS - Re-opened Applications - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine.
-how_to_implement = In order to properly run this search, Splunk needs to ingest process data from your osquery deployed agents with the [splunk.conf](https://github.com/splunk/TA-osquery/blob/master/config/splunk.conf) pack enabled. Also the [TA-OSquery](https://github.com/splunk/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the data populate the Endpoint data model.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "DE.CM"]}
-known_false_positives = At this stage, there are no known false positives. During testing, no process events refering the com.apple.loginwindow.plist files were observed during normal operation of re-opening applications on reboot. Therefore, it can be asumed that any occurences of this in the process events would be worth investigating. In the event that the legitimate modification by the system of these files is in fact logged to the process log, then the process_name of that process can be added to an allow list.
-providing_technologies = []
-
-[savedsearch://ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. Deprecated becaue hidden is not needed when download file with System.Net.WebClient.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
-known_false_positives = Legitimate process can have this combination of command-line options, but it's not common.
-providing_technologies = []
-
-[savedsearch://ESCU - Malicious PowerShell Process - Encoded Command - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1027"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
-known_false_positives = System administrators may use this option, but it's not common.
-providing_technologies = []
-
-[savedsearch://ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
-known_false_positives = There may be legitimate reasons to bypass the PowerShell execution policy. The PowerShell script being run with this parameter should be validated to ensure that it is legitimate.
-providing_technologies = []
-
-[savedsearch://ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. Deprecated because almost the same as Malicious PowerShell Process - Encoded Command
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
-known_false_positives = Legitimate process can have this combination of command-line options, but it's not common.
-providing_technologies = []
-
-[savedsearch://ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1059.001"], "nist": ["PR.PT", "DE.CM", "PR.IP"]}
-known_false_positives = These characters might be legitimately on the command-line, but it is not common.
-providing_technologies = []
-
-[savedsearch://ESCU - Monitor DNS For Brand Abuse - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse.
-how_to_implement = You need to ingest data from your DNS logs. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for.
-annotations = {"kill_chain_phases": ["Delivery", "Actions on Objectives"]}
-known_false_positives = None at this time
-providing_technologies = []
-
-[savedsearch://ESCU - Monitor Email For Brand Abuse - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse.
-how_to_implement = You need to ingest email header data. Specifically the sender's address (src_user) must be populated. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for.
-annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]}
-known_false_positives = None at this time
-providing_technologies = []
-
-[savedsearch://ESCU - Monitor Registry Keys for Print Monitors - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot.
-how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications.
-annotations = {"cis20": ["CIS 8", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1547.010"], "nist": ["PR.PT", "DE.CM", "PR.AC"]}
-known_false_positives = You will encounter noise from legitimate print-monitor registry entries.
-providing_technologies = []
-
-[savedsearch://ESCU - Monitor Web Traffic For Brand Abuse - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse.
-how_to_implement = You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for.
-annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]}
-known_false_positives = None at this time
-providing_technologies = []
-
-[savedsearch://ESCU - Multiple Okta Users With Invalid Credentials From The Same IP - Rule]
-type = detection
-asset_type = Infrastructure
-confidence = medium
-explanation = This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address.
-how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.
-annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078.001"], "nist": ["DE.CM"]}
-known_false_positives = A single public IP address servicing multiple legitmate users may trigger this search. In addition, the threshold of 5 distinct users may be too low for your needs. You may modify the included filter macro `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` to raise the threshold or except specific IP adresses from triggering this search.
-providing_technologies = []
-
-[savedsearch://ESCU - NLTest Domain Trust Discovery - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for the execution of `nltest.exe` with command-line arguments utilized to query for Domain Trust information. Two arguments `/domain trusts`, returns a list of trusted domains, and `/all_trusts`, returns all trusted domains. Red Teams and adversaries alike use NLTest.exe to enumerate the current domain to assist with further understanding where to pivot next.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1482"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Administrators may use nltest for troubleshooting purposes, otherwise, rarely used.
-providing_technologies = []
-
-[savedsearch://ESCU - New container uploaded to AWS ECR - Rule]
-type = detection
-asset_type = AWS ECR container
-confidence = medium
-explanation = This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_aws_detection_filter` macro to filter out the false positives.
-annotations = {"mitre_attack": ["T1525"]}
-known_false_positives = Uploading container is a normal behavior from developers or users with access to container registry.
-providing_technologies = []
-
-[savedsearch://ESCU - Nishang PowershellTCPOneLine - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = This query detects the Nishang Invoke-PowerShellTCPOneLine utility that spawns a call back to a remote command and control server. This is a powershell oneliner. In addition, this will capture on the command-line additional utilities used by Nishang. Triage the endpoint and identify any parallel processes that look suspicious. Review the reputation of the remote IP or domain contacted by the powershell process.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059.001"]}
-known_false_positives = Limited false positives may be present. Filter as needed based on initial analysis.
-providing_technologies = []
-
-[savedsearch://ESCU - No Windows Updates in a time frame - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason.
-how_to_implement = To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems.
-annotations = {"cis20": ["CIS 18"], "nist": ["PR.PT", "PR.MA"]}
-known_false_positives = None identified
-providing_technologies = []
-
-[savedsearch://ESCU - Ntdsutil Export NTDS - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = Monitor for signs that Ntdsutil is being used to Extract Active Directory database - NTDS.dit, typically used for offline password cracking. It may be used in normal circumstances with no command line arguments or shorthand variations of more common arguments. Ntdsutil.exe is typically seen run on a Windows Server. Typical command used to dump ntds.dit \
-ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \
-This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination.
-how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.003"], "nist": ["DE.CM"]}
-known_false_positives = Highly possible Server Administrators will troubleshoot with ntdsutil.exe, generating false positives.
-providing_technologies = []
-
[savedsearch://ESCU - O365 Add App Role Assignment Grant User - Rule]
type = detection
asset_type = Office 365
@@ -3686,1155 +667,6 @@ annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives
known_false_positives = unknown
providing_technologies = []
-[savedsearch://ESCU - Okta Account Lockout Events - Rule]
-type = detection
-asset_type = Infrastructure
-confidence = medium
-explanation = Detect Okta user lockout events
-how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.
-annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078.001"], "nist": ["DE.CM"]}
-known_false_positives = None. Account lockouts should be followed up on to determine if the actual user was the one who caused the lockout, or if it was an unauthorized actor.
-providing_technologies = []
-
-[savedsearch://ESCU - Okta Failed SSO Attempts - Rule]
-type = detection
-asset_type = Infrastructure
-confidence = medium
-explanation = Detect failed Okta SSO events
-how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.
-annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078.001"], "nist": ["DE.CM"]}
-known_false_positives = There may be a faulty config preventing legitmate users from accessing apps they should have access to.
-providing_technologies = []
-
-[savedsearch://ESCU - Okta User Logins From Multiple Cities - Rule]
-type = detection
-asset_type = Infrastructure
-confidence = medium
-explanation = This search detects logins from the same user from different cities in a 24 hour period.
-how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.
-annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078.001"], "nist": ["DE.CM"]}
-known_false_positives = Users in your enviornment may legitmately be travelling and loggin in from different locations. This search is useful for those users that should *not* be travelling for some reason, such as the COVID-19 pandemic. The search also relies on the geographical information being populated in the Okta logs. It is also possible that a connection from another region may be attributed to a login from a remote VPN endpoint.
-providing_technologies = []
-
-[savedsearch://ESCU - Open Redirect in Splunk Web - Rule]
-type = detection
-asset_type = Splunk Server
-confidence = medium
-explanation = This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability.
-how_to_implement = No extra steps needed to implement this search.
-annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]}
-known_false_positives = None identified
-providing_technologies = []
-
-[savedsearch://ESCU - Osquery pack - ColdRoot detection - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for ColdRoot events from the osx-attacks osquery pack.
-how_to_implement = In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model
-annotations = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "DE.CM", "PR.PT"]}
-known_false_positives = There are no known false positives.
-providing_technologies = []
-
-[savedsearch://ESCU - Overwriting Accessibility Binaries - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries.
-how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.008"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Microsoft may provide updates to these binaries. Verify that these changes do not correspond with your normal software update cycle.
-providing_technologies = []
-
-[savedsearch://ESCU - Process Creating LNK file in Suspicious Location - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for a process launching an `*.lnk` file under `C:\User*` or `*\Local\Temp\*`. This is common behavior used by various spear phishing tools.
-how_to_implement = You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon.
-annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1566.002"], "nist": ["ID.AM", "PR.DS"]}
-known_false_positives = This detection should yield little or no false positive results. It is uncommon for LNK files to be executed from temporary or user directories.
-providing_technologies = []
-
-[savedsearch://ESCU - Process Deleting Its Process File Path - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = This detection is to identify a suspicious process that tries to delete the process file path related to its process. This technique is known to be defense evasion once a certain condition of malware is satisfied or not. Clop ransomware use this technique where it will try to delete its process file path using a .bat command if the keyboard layout is not the layout it tries to infect.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1003.002"]}
-known_false_positives = unknown
-providing_technologies = []
-
-[savedsearch://ESCU - Process Execution via WMI - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for processes launched via WMI.
-how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
-known_false_positives = Although unlikely, administrators may use wmi to execute commands for legitimate purposes.
-providing_technologies = []
-
-[savedsearch://ESCU - Processes Tapping Keyboard Events - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input
-how_to_implement = In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model.
-annotations = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Command and Control"], "nist": ["DE.DP"]}
-known_false_positives = There might be some false positives as keyboard event taps are used by processes like Siri and Zoom video chat, for some good examples of processes to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161) comment.
-providing_technologies = []
-
-[savedsearch://ESCU - Processes created by netsh - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. Deprecated because we have another detection of the same type.
-how_to_implement = To successfully implement this search, you must be ingesting logs with the process name, command-line arguments, and parent processes from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.004"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate. We explicitely exclude "C:\Program Files\rempl\sedlauncher.exe" process path since it is a legitimate process by Mircosoft.
-providing_technologies = []
-
-[savedsearch://ESCU - Processes launching netsh - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line.
-how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.004"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands.
-providing_technologies = []
-
-[savedsearch://ESCU - Prohibited Network Traffic Allowed - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device.
-how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated.
-annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1048"], "nist": ["DE.AE", "PR.AC"]}
-known_false_positives = None identified
-providing_technologies = []
-
-[savedsearch://ESCU - Prohibited Software On Endpoint - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for applications on the endpoint that you have marked as prohibited.
-how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report process tracking in your Windows audit settings. In addition, you must also have only the `process_name` (not the entire process path) marked as "prohibited" in the Enterprise Security `interesting processes` table. To include the process names marked as "prohibited", which is included with ES Content Updates, run the included search Add Prohibited Processes to Enterprise Security.
-annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]}
-known_false_positives = None identified
-providing_technologies = []
-
-[savedsearch://ESCU - Protocol or Port Mismatch - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for network traffic on common ports where a higher layer protocol does not match the port that is being used. For example, this search should identify cases where protocols other than HTTP are running on TCP port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications over ports and protocols that are typically allowed and not well inspected.
-how_to_implement = Running this search properly requires a technology that can inspect network traffic and identify common protocols. Technologies such as Bro and Palo Alto Networks firewalls are two examples that will identify protocols via inspection, and not just assume a specific protocol based on the transport protocol and ports.
-annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048.003"], "nist": ["DE.AE", "PR.AC"]}
-known_false_positives = None identified
-providing_technologies = []
-
-[savedsearch://ESCU - Protocols passing authentication in cleartext - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases.
-how_to_implement = This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model.
-annotations = {"cis20": ["CIS 9", "CIS 14"], "kill_chain_phases": ["Reconnaissance", "Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "PR.AC", "PR.DS"]}
-known_false_positives = Some networks may use kerberized FTP or telnet servers, however, this is rare.
-providing_technologies = []
-
-[savedsearch://ESCU - Ransomware Notes bulk creation - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = The following analytics identifies a big number of instance of ransomware notes (filetype e.g .txt, .html, .hta) file creation to the infected machine. This behavior is a good sensor if the ransomware note filename is quite new for security industry or the ransomware note filename is not in your lookup table list for monitoring.
-how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
-annotations = {"kill_chain_phases": ["Obfuscation"], "mitre_attack": ["T1486"]}
-known_false_positives = unknown
-providing_technologies = []
-
-[savedsearch://ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The search looks for reg.exe modifying registry keys that define Windows services and their configurations.
-how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
-annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1574.011"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]}
-known_false_positives = It is unusual for a service to be created or modified by directly manipulating the registry. However, there may be legitimate instances of this behavior. It is important to validate and investigate, as appropriate.
-providing_technologies = []
-
-[savedsearch://ESCU - Reg exe used to hide files directories via registry keys - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = The search looks for command-line arguments used to hide a file or directory using the reg add command.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1564.001"], "nist": ["DE.CM"]}
-known_false_positives = None at the moment
-providing_technologies = []
-
-[savedsearch://ESCU - Registry Keys Used For Persistence - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The search looks for modifications to registry keys that can be used to launch an application or service at system startup.
-how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1547.001"], "nist": ["PR.PT", "DE.CM", "DE.AE"]}
-known_false_positives = There are many legitimate applications that must execute on system startup and will use these registry keys to accomplish that task.
-providing_technologies = []
-
-[savedsearch://ESCU - Registry Keys Used For Privilege Escalation - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries.
-how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.012"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = There are many legitimate applications that must execute upon system startup and will use these registry keys to accomplish that task.
-providing_technologies = []
-
-[savedsearch://ESCU - Registry Keys for Creating SHIM Databases - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes.
-how_to_implement = To successfully implement this search, you must populate the Change_Analysis data model. This is typically populated via endpoint detection and response product, such as Carbon Black or other endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.011"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications
-providing_technologies = []
-
-[savedsearch://ESCU - Remote Desktop Network Bruteforce - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic.
-how_to_implement = You must ensure that your network traffic data is populating the Network_Traffic data model.
-annotations = {"cis20": ["CIS 12", "CIS 9", "CIS 16"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "mitre_attack": ["T1021.001"], "nist": ["DE.AE", "PR.AC", "PR.IP"]}
-known_false_positives = RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network.
-providing_technologies = []
-
-[savedsearch://ESCU - Remote Desktop Network Traffic - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search will ignore common RDP sources and common RDP destinations so you can focus on the uncommon uses of remote desktop on your network.
-how_to_implement = To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups.
-annotations = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.001"], "nist": ["DE.AE", "PR.AC", "PR.IP"]}
-known_false_positives = Remote Desktop may be used legitimately by users on the network.
-providing_technologies = []
-
-[savedsearch://ESCU - Remote Desktop Process Running On System - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework.
-how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly use remote desktop. You can use the included support search "Identify Systems Using Remote Desktop" to identify these systems. After identifying them, you will need to add the "common_rdp_source" category to that system using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in `SA-IdentityManagement/lookups`.
-annotations = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.001"], "nist": ["DE.AE", "PR.AC", "PR.IP"]}
-known_false_positives = Remote Desktop may be used legitimately by users on the network.
-providing_technologies = []
-
-[savedsearch://ESCU - Remote Process Instantiation via WMI - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for wmic.exe being launched with parameters to spawn a process on a remote system.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
-known_false_positives = The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon.
-providing_technologies = []
-
-[savedsearch://ESCU - Remote Registry Key modifications - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search monitors for remote modifications to registry keys.
-how_to_implement = To successfully implement this search, you must populate the `Endpoint` data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. Deprecated because I don't think the logic is right.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out.
-providing_technologies = []
-
-[savedsearch://ESCU - Remote WMI Command Attempt - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for wmic.exe being launched with parameters to operate on remote systems.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. Deprecated because duplicate of Remote Process Instantiation via WMI.
-annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
-known_false_positives = Administrators may use this legitimately to gather info from remote systems.
-providing_technologies = []
-
-[savedsearch://ESCU - Resize ShadowStorage volume - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = The following analytics identifies the resizing of shadowstorage by ransomware malware to avoid the shadow volumes being made again. this technique is an alternative by ransomware attacker than deleting the shadowstorage which is known alert in defensive team. one example of ransomware that use this technique is CLOP ransomware where it drops a .bat file that will resize the shadowstorage to minimum size as much as possible
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1490"]}
-known_false_positives = network admin can resize the shadowstorage for valid purposes.
-providing_technologies = []
-
-[savedsearch://ESCU - RunDLL Loading DLL By Ordinal - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for executing scripts with rundll32. Adversaries may abuse rundll32.exe to proxy execution of malicious code. Using rundll32.exe, vice executing directly, may avoid triggering security tools that may not monitor execution of the rundll32.exe process because of allowlists or false positives from normal operations.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = While not common, loading a DLL under %AppData% and calling a function by ordinal is possible by a legitimate process
-providing_technologies = []
-
-[savedsearch://ESCU - Ryuk Test Files Detected - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The search looks for files that contain the key word *Ryuk* under any folder in the C drive, which is consistent with Ryuk propagation.
-how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint Filesystem data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1486"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = If there are files with this keywoord as file names it might trigger false possitives, please make use of our filters to tune out potential FPs.
-providing_technologies = []
-
-[savedsearch://ESCU - Ryuk Wake on LAN Command - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = This Splunk query identifies the use of Wake-on-LAN utilized by Ryuk ransomware. The Ryuk Ransomware uses the Wake-on-Lan feature to turn on powered off devices on a compromised network to have greater success encrypting them. This is a high fidelity indicator of Ryuk ransomware executing on an endpoint. Upon triage, isolate the endpoint. Additional file modification events will be within the users profile (\appdata\roaming) and in public directories (users\public\). Review all Scheduled Tasks on the isolated endpoint and across the fleet. Suspicious Scheduled Tasks will include a path to a unknown binary and those endpoints should be isolated until triaged.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"kill_chain_phases": ["Exploitation", "Lateral Movement"], "mitre_attack": ["T1059.003"]}
-known_false_positives = Limited to no known false positives.
-providing_technologies = []
-
-[savedsearch://ESCU - SMB Traffic Spike - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for spikes in the number of Server Message Block (SMB) traffic connections.
-how_to_implement = This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.002"], "nist": ["DE.CM"]}
-known_false_positives = A file server may experience high-demand loads that could cause this analytic to trigger.
-providing_technologies = []
-
-[savedsearch://ESCU - SMB Traffic Spike - MLTK - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections.
-how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of SMB Traffic - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\
-This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry): \
-1. **Label:** Number of events, **Field:** count\
-Detailed documentation on how to create a new field within Incident Review is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1021.002"], "nist": ["DE.CM"]}
-known_false_positives = If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. Please update the `smb_traffic_spike_mltk_filter` macro to filter out false positive results
-providing_technologies = []
-
-[savedsearch://ESCU - SQL Injection with Long URLs - Rule]
-type = detection
-asset_type = Database Server
-confidence = medium
-explanation = This search looks for long URLs that have several SQL commands visible within them.
-how_to_implement = To successfully implement this search, you need to be monitoring network communications to your web servers or ingesting your HTTP logs and populating the Web data model. You must also identify your web servers in the Enterprise Security assets table.
-annotations = {"cis20": ["CIS 4", "CIS 13", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1190"], "nist": ["PR.DS", "ID.RA", "PR.PT", "PR.IP", "DE.CM"]}
-known_false_positives = It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate.
-providing_technologies = []
-
-[savedsearch://ESCU - Samsam Test File Write - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation.
-how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1486"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = No false positives have been identified.
-providing_technologies = []
-
-[savedsearch://ESCU - Sc exe Manipulating Windows Services - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for arguments to sc.exe indicating the creation or modification of a Windows service.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1543.003"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]}
-known_false_positives = Using sc.exe to manipulate Windows services is uncommon. However, there may be legitimate instances of this behavior. It is important to validate and investigate as appropriate.
-providing_technologies = []
-
-[savedsearch://ESCU - Scheduled Task Deleted Or Created via CMD - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for flags passed to schtasks.exe on the command-line that indicate a task was created via command like. This has been associated with the Dragonfly threat actor, and the SUNBURST attack against Solarwinds.
-how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005"], "nist": ["PR.IP"]}
-known_false_positives = Tasks should not be manually created via CLI, this is rarely done by admins as well
-providing_technologies = []
-
-[savedsearch://ESCU - Scheduled tasks used in BadRabbit ransomware - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. Deprecated because we already have a similar detection
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005"], "nist": ["PR.IP"]}
-known_false_positives = No known false positives
-providing_technologies = []
-
-[savedsearch://ESCU - Schtasks scheduling job on remote system - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005"], "nist": ["PR.IP"]}
-known_false_positives = Administrators may create jobs on remote systems, but this activity is usually limited to a small set of hosts or users. It is important to validate and investigate as appropriate.
-providing_technologies = []
-
-[savedsearch://ESCU - Schtasks used for forcing a reboot - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled.
-how_to_implement = To successfully implement this search you need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053.005"], "nist": ["PR.IP"]}
-known_false_positives = Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc.
-providing_technologies = []
-
-[savedsearch://ESCU - Script Execution via WMI - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for scripts launched via WMI.
-how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
-known_false_positives = Although unlikely, administrators may use wmi to launch scripts for legitimate purposes.
-providing_technologies = []
-
-[savedsearch://ESCU - Shim Database File Creation - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere.
-how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.011"], "nist": ["DE.CM"]}
-known_false_positives = Because legitimate shim files are created and used all the time, this event, in itself, is not suspicious. However, if there are other correlating events, it may warrant further investigation.
-providing_technologies = []
-
-[savedsearch://ESCU - Shim Database Installation With Suspicious Parameters - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.011"], "nist": ["DE.CM"]}
-known_false_positives = None identified
-providing_technologies = []
-
-[savedsearch://ESCU - Short Lived Windows Accounts - Rule]
-type = detection
-asset_type = Windows
-confidence = medium
-explanation = This search detects accounts that were created and deleted in a short time period.
-how_to_implement = This search requires you to have enabled your Group Management Audit Logs in your Local Windows Security Policy and be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/
-annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1136.001"], "nist": ["PR.IP"]}
-known_false_positives = It is possible that an administrator created and deleted an account in a short time period. Verifying activity with an administrator is advised.
-providing_technologies = []
-
-[savedsearch://ESCU - Single Letter Process On Endpoint - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for process names that consist only of a single letter.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.002"], "nist": ["ID.AM", "PR.DS"]}
-known_false_positives = Single-letter executables are not always malicious. Investigate this activity with your normal incident-response process.
-providing_technologies = []
-
-[savedsearch://ESCU - Spectre and Meltdown Vulnerable Systems - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities.
-how_to_implement = The search requires that you are ingesting your vulnerability-scanner data and that it reports the CVE of the vulnerability identified.
-annotations = {"cis20": ["CIS 4"], "nist": ["ID.RA", "RS.MI", "PR.IP", "DE.CM"]}
-known_false_positives = It is possible that your vulnerability scanner is not detecting that the patches have been applied.
-providing_technologies = []
-
-[savedsearch://ESCU - Spike in File Writes - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The search looks for a sharp increase in the number of files written to a particular host
-how_to_implement = In order to implement this search, you must populate the Endpoint file-system data model node. This is typically populated via endpoint detection and response product, such as Carbon Black or endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the file system.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"]}
-known_false_positives = It is important to understand that if you happen to install any new applications on your hosts or are copying a large number of files, you can expect to see a large increase of file modifications.
-providing_technologies = []
-
-[savedsearch://ESCU - Splunk Enterprise Information Disclosure - Rule]
-type = detection
-asset_type = Splunk Server
-confidence = medium
-explanation = This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug.
-how_to_implement = The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Whitelisting your Splunk systems will reduce false positives.
-annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]}
-known_false_positives = Retrieving server information may be a legitimate API request. Verify that the attempt is a valid request for information.
-providing_technologies = []
-
-[savedsearch://ESCU - Sunburst Correlation DLL and Network Event - Rule]
-type = detection
-asset_type = Windows
-confidence = medium
-explanation = The malware sunburst will load the malicious dll by SolarWinds.BusinessLayerHost.exe. After a period of 12-14 days, the malware will attempt to resolve a subdomain of avsvmcloud.com. This detections will correlate both events.
-how_to_implement = This detection relies on sysmon logs with the Event ID 7, Driver loaded. Please tune your sysmon config that you DriverLoad event for SolarWinds.Orion.Core.BusinessLayer.dll is captured by Sysmon. Additionally, you need sysmon logs for Event ID 22, DNS Query. We suggest to run this detection at least once a day over the last 14 days.
-annotations = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1203"], "nist": ["DE.CM"]}
-known_false_positives = unknown
-providing_technologies = []
-
-[savedsearch://ESCU - Supernova Webshell - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = This search aims to detect the Supernova webshell used in the SUNBURST attack.
-how_to_implement = To successfully implement this search, you need to be monitoring web traffic to your Solarwinds Orion. The logs should be ingested into splunk and populating/mapped to the Web data model.
-annotations = {"cis20": ["CIS 4", "CIS 13", "CIS 18"], "kill_chain_phases": ["Exfiltration"], "mitre_attack": ["T1505.003"], "nist": ["PR.DS", "ID.RA", "PR.PT", "PR.IP", "DE.CM"]}
-known_false_positives = There might be false positives associted with this detection since items like args as a web argument is pretty generic.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious Changes to File Associations - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area.
-how_to_implement = To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes.
-annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.001"], "nist": ["DE.CM", "PR.PT", "PR.IP"]}
-known_false_positives = There may be other processes in your environment that users may legitimately use to modify file associations. If this is the case and you are finding false positives, you can modify the search to add those processes as exceptions.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious Curl Network Connection - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies the use of a curl contacting suspicious remote domains to checkin to command and control servers or download further implants. In the context of Silver Sparrow, curl is identified contacting s3.amazonaws.com. This particular behavior is common with MacOS adware-malicious software.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1105"]}
-known_false_positives = Unknown. Filter as needed.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious DLLHost no Command Line Arguments - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = The following analytic identifies DLLHost.exe with no command line arguments. It is unusual for DLLHost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. DLLHost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"]}
-known_false_positives = Limited false positives may be present in small environments. Tuning may be required based on parent process.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious Email - UBA Anomaly - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA).
-how_to_implement = You must be ingesting data from email logs and have Splunk integrated with UBA. This anomaly is raised by a UBA detection model called "SuspiciousEmailDetectionModel." Ensure that this model is enabled on your UBA instance.
-annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1566"], "nist": ["PR.IP"]}
-known_false_positives = This detection model will alert on any sender domain that is seen for the first time. This could be a potential false positive. The next step is to investigate and add the URL to an allow list if you determine that it is a legitimate sender.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious Email Attachment Extensions - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for emails that have attachments with suspicious file extensions.
-how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. \
- **Splunk Phantom Playbook Integration**\
-If Splunk Phantom is also configured in your environment, a Playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox.
-annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 12"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1566.001"], "nist": ["DE.AE", "PR.IP"]}
-known_false_positives = None identified
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious File Write - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The search looks for files created with names that have been linked to malicious activity.
-how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. In addition, this search leverages an included lookup file that contains the names of the files to watch for, as well as a note to communicate why that file name is being monitored. This lookup file can be edited to add or remove file the file names you want to monitor.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = It's possible for a legitimate file to be created with the same name as one noted in the lookup file. Filenames listed in the lookup file should be unique enough that collisions are rare. Looking at the location of the file and the process responsible for the activity can help determine whether or not the activity is legitimate.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious GPUpdate no Command Line Arguments - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = The following analytic identifies gpupdate.exe with no command line arguments. It is unusual for gpupdate.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. gpupdate.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"]}
-known_false_positives = Limited false positives may be present in small environments. Tuning may be required based on parent process.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious Java Classes - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts.
-how_to_implement = In order to properly run this search, Splunk needs to ingest data from your web-traffic appliances that serve or sit in the path of your Struts application servers. This can be accomplished by indexing data from a web proxy, or by using network traffic-analysis tools, such as Splunk Stream or Bro.
-annotations = {"cis20": ["CIS 7", "CIS 12"], "kill_chain_phases": ["Exploitation"], "nist": ["DE.AE"]}
-known_false_positives = There are no known false positives.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious MSBuild Rename - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies renamed instances of msbuild.exe executing. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. During investigation, identify the code executed and what is executing a renamed instance of MSBuild.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127.001", "T1036.003"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, some legitimate applications may use a moved copy of msbuild, triggering a false positive.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious MSBuild Spawn - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies wmiprvse.exe spawning msbuild.exe. This behavior is indicative of a COM object being utilized to spawn msbuild from wmiprvse.exe. It is common for MSBuild.exe to be spawned from devenv.exe while using Visual Studio. In this instance, there will be command line arguments and file paths. In a malicious instance, MSBuild.exe will spawn from non-standard processes and have no command line arguments. For example, MSBuild.exe spawning from explorer.exe, powershell.exe is far less common and should be investigated.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127.001"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious PlistBuddy Usage - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies the use of a native MacOS utility, PlistBuddy, creating or modifying a properly list (.plist) file. In the instance of Silver Sparrow, the following commands were executed:\
-- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \
-- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \
-- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \
-- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \
-- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \
-- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \
-Upon triage, capture the property list file being written to disk and review for further indicators. Contain the endpoint and triage further.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1543.001"]}
-known_false_positives = Some legitimate applications may use PlistBuddy to create or modify property lists and possibly generate false positives. Review the property list being modified or created to confirm.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious PlistBuddy Usage via OSquery - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies the use of a native MacOS utility, PlistBuddy, creating or modifying a properly list (.plist) file. In the instance of Silver Sparrow, the following commands were executed:\
-- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \
-- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \
-- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \
-- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \
-- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \
-- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \
-Upon triage, capture the property list file being written to disk and review for further indicators. Contain the endpoint and triage further.
-how_to_implement = OSQuery must be installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. Modify the macro and validate fields are correct.
-annotations = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1543.001"]}
-known_false_positives = Some legitimate applications may use PlistBuddy to create or modify property lists and possibly generate false positives. Review the property list being modified or created to confirm.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious Reg exe Process - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1112"], "nist": ["DE.CM"]}
-known_false_positives = It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious Regsvr32 Register Suspicious Path - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using non-standard file extensions to load malciious DLLs. Upon investigating, look for network connections to remote destinations (internal or external). Review additional parrallel processes and child processes for additional activity.
-how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Tune the query by filtering additional extensions found to be used by legitimate processes.
-annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.010"], "nist": ["DE.CM"]}
-known_false_positives = Limited false positives with the query restricted to specified paths. Add more world writeable paths as tuning continues.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious Rundll32 Rename - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies renamed instances of rundll32.exe executing. rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, validate it is the legitimate rundll32.exe executing and what script content it is loading. This query relies on the OriginalFileName from Sysmon, or internal name from the PE meta data. Expand the query as needed by looking for specific command line arguments outlined in other analytics.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011", "T1036.003"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious Rundll32 StartW - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies rundll32.exe executing a DLL function name, Start and StartW, on the command line that is commonly observed with Cobalt Strike x86 and x64 DLL payloads. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. Typically, the DLL will be written and loaded from a world writeable path or user location. In most instances it will not have a valid certificate (Unsigned). During investigation, review the parent process and other parallel application execution. Capture and triage the DLL in question. In the instance of Cobalt Strike, rundll32.exe is the default process it opens and injects shellcode into. This default process can be changed, but typically is not.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, some legitimate applications may use Start as a function and call it via the command line. Filter as needed.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious Rundll32 dllregisterserver - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies rundll32.exe using dllregisterserver on the command line to load a DLL. When a DLL is registered, the DllRegisterServer method entry point in the DLL is invoked. This is typically seen when a DLL is being registered on the system. Not every instance is considered malicious, but it will capture malicious use of it. During investigation, review the parent process and parrellel processes executing. Capture the DLL being loaded and inspect further. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = This is likely to produce false positives and will require some filtering. Tune the query by adding command line paths to known good DLLs, or filtering based on parent process names.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious Rundll32 no Command Line Arguments - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies rundll32.exe with no command line arguments. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1218.011"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious SQLite3 LSQuarantine Behavior - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies the use of a SQLite3 querying the MacOS preferences to identify the original URL the pkg was downloaded from. This particular behavior is common with MacOS adware-malicious software. Upon triage, review other processes in parallel for suspicious activity. Identify any recent package installations.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1074"]}
-known_false_positives = Unknown.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious Scheduled Task from Public Directory - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = The following detection identifies Scheduled Tasks registering (creating a new task) a binary or script to run from a public directory which includes users\public, \programdata\ and \windows\temp. Upon triage, review the binary or script in the command line for legitimacy, whether an approved binary/script or not. In addition, capture the binary or script in question and analyze for further behaviors. Identify the source and contain the endpoint.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"kill_chain_phases": ["Exploitation", "Privilege Escalation"], "mitre_attack": ["T1053.005"]}
-known_false_positives = Limited false positives may be present. Filter as needed by parent process or command line argument.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious SearchProtocolHost no Command Line Arguments - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = The following analytic identifies searchprotocolhost.exe with no command line arguments. It is unusual for searchprotocolhost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. searchprotocolhost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1055"]}
-known_false_positives = Limited false positives may be present in small environments. Tuning may be required based on parent process.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious microsoft workflow compiler rename - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies a renamed instance of microsoft.workflow.compiler.exe. Microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. A spawned child process from microsoft.workflow.compiler.exe is uncommon. In any instance, microsoft.workflow.compiler.exe spawning from an Office product or any living off the land binary is highly suspect.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127", "T1036.003"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, some legitimate applications may use a moved copy of microsoft.workflow.compiler.exe, triggering a false positive.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious microsoft workflow compiler usage - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies microsoft.workflow.compiler.exe usage. microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. It is not a commonly used process by many applications.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, limited instances have been identified coming from native Microsoft utilities similar to SCCM.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious msbuild path - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies msbuild.exe executing from a non-standard path. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. Instances of Visual Studio will run a copy of msbuild.exe. A moved instance of MSBuild is suspicious, however there are instances of build applications that will move or use a copy of MSBuild.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1127.001", "T1036.003"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Some legitimate applications may use a moved copy of msbuild.exe, triggering a false positive. Baselining of MSBuild.exe usage is recommended to better understand it's path usage. Visual Studio runs an instance out of a path that will need to be filtered on.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious mshta child process - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies child processes spawning from "mshta.exe". The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, parent process "mshta.exe" and its child process.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious mshta spawn - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The following analytic identifies wmiprvse.exe spawning mshta.exe. This behavior is indicative of a DCOM object being utilized to spawn mshta from wmiprvse.exe or svchost.exe. In this instance, adversaries may use LethalHTA that will spawn mshta.exe from svchost.exe.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1218.005"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious wevtutil Usage - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070.001"], "nist": ["DE.DP", "PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.AE"]}
-known_false_positives = The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious writes to System Volume Information - Rule]
-type = detection
-asset_type = Windows
-confidence = medium
-explanation = This search detects writes to the 'System Volume Information' folder by something other than the System process.
-how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"cis20": ["CIS 8"], "mitre_attack": ["T1036"], "nist": ["DE.CM"]}
-known_false_positives = It is possible that other utilities or system processes may legitimately write to this folder. Investigate and modify the search to include exceptions as appropriate.
-providing_technologies = []
-
-[savedsearch://ESCU - Suspicious writes to windows Recycle Bin - Rule]
-type = detection
-asset_type = Windows
-confidence = medium
-explanation = This search detects writes to the recycle bin by a process other than explorer.exe.
-how_to_implement = To successfully implement this search you need to be ingesting information on filesystem and process logs responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes.
-annotations = {"cis20": ["CIS 8"], "mitre_attack": ["T1036"], "nist": ["DE.CM"]}
-known_false_positives = Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate.
-providing_technologies = []
-
-[savedsearch://ESCU - System Information Discovery Detection - Rule]
-type = detection
-asset_type = Windows
-confidence = medium
-explanation = Detect system information discovery techniques used by attackers to understand configurations of the system to further exploit it.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1082"], "nist": ["DE.CM"]}
-known_false_positives = Administrators debugging servers
-providing_technologies = []
-
-[savedsearch://ESCU - System Processes Run From Unexpected Locations - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for system processes that normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location. This can indicate a malicious process that is trying to hide as a legitimate process.
-how_to_implement = To successfully implement this search you need to ingest details about process execution from your hosts. Specifically, this search requires the process name and the full path to the process executable.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036.003"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = None identified
-providing_technologies = []
-
-[savedsearch://ESCU - TOR Traffic - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes.
-how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated.
-annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1071.001"], "nist": ["DE.AE"]}
-known_false_positives = None at this time
-providing_technologies = []
-
-[savedsearch://ESCU - USN Journal Deletion - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-annotations = {"cis20": ["CIS 6", "CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.CM", "PR.PT", "DE.AE", "DE.DP", "PR.IP"]}
-known_false_positives = None identified
-providing_technologies = []
-
-[savedsearch://ESCU - Uncommon Processes On Endpoint - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = This search looks for applications on the endpoint that you have marked as uncommon.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search uses a lookup file `uncommon_processes_default.csv` to track various features of process names that are usually uncommon in most environments. Please consider updating `uncommon_processes_local.csv` to hunt for processes that are uncommon in your environment.
-annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1204.002"], "nist": ["ID.AM", "PR.DS"]}
-known_false_positives = None identified
-providing_technologies = []
-
-[savedsearch://ESCU - Unified Messaging Service Spawning a Process - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = This detection identifies Microsoft Exchange Server's Unified Messaging services, umworkerprocess.exe and umservice.exe, spawning a child process, indicating possible exploitation of CVE-2021-26857 vulnerability. The query filters out werfault.exe and wermgr.exe mostly due to potential false positives, however, if there is an excessive amount of "wermgr.exe" or "WerFault.exe" failures, it may be due to the active exploitation. During triage, identify any additional suspicious parallel processes. Identify any recent out of place file modifications. Review Exchange logs following Microsofts guide. To contain, perform egress filtering or restrict public access to Exchange. In final, patch the vulnerablity and monitor.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1190"]}
-known_false_positives = Unknown. Tune out child processes as needed to limit volume of false positives.
-providing_technologies = []
-
-[savedsearch://ESCU - Unload Sysmon Filter Driver - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro, update this macro to filter out false positives.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1562.001"], "nist": ["DE.CM"]}
-known_false_positives =
-providing_technologies = []
-
-[savedsearch://ESCU - Unsigned Image Loaded by LSASS - Rule]
-type = detection
-asset_type = Windows
-confidence = medium
-explanation = This search detects loading of unsigned images by LSASS. Deprecated because too noisy.
-how_to_implement = This search needs Sysmon Logs with a sysmon configuration, which includes EventCode 7 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
-annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003.001"], "nist": ["DE.CM"]}
-known_false_positives = Other tools could load images into LSASS for legitimate reason. But enterprise tools should always use signed DLLs.
-providing_technologies = []
-
-[savedsearch://ESCU - Unsuccessful Netbackup backups - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search gives you the hosts where a backup was attempted and then failed.
-how_to_implement = To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution.
-annotations = {"cis20": ["CIS 10"], "nist": ["PR.IP"]}
-known_false_positives = None identified
-providing_technologies = []
-
-[savedsearch://ESCU - Unusually Long Command Line - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = Command lines that are extremely long may be indicative of malicious activity on your hosts.
-how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships, from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the process field in the Endpoint data model.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Some legitimate applications start with long command lines.
-providing_technologies = []
-
-[savedsearch://ESCU - Unusually Long Command Line - MLTK - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user.
-how_to_implement = You must be ingesting endpoint data that monitors command lines and populates the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, MLTK version >= 4.2 must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of Command Line Length - MLTK" must be executed before this detection search, as it builds an ML model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = Some legitimate applications use long command lines for installs or updates. You should review identified command lines for legitimacy. You may modify the first part of the search to omit legitimate command lines from consideration. If you are seeing more results than desired, you may consider changing the value of threshold in the search to a smaller value. You should also periodically re-run the support search to re-build the ML model on the latest data. You may get unexpected results if the user identified in the results is not present in the data used to build the associated model.
-providing_technologies = []
-
-[savedsearch://ESCU - Unusually Long Content-Type Length - Rule]
-type = detection
-asset_type = Web Server
-confidence = medium
-explanation = This search looks for unusually long strings in the Content-Type http header that the client sends the server.
-how_to_implement = This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field.
-annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18", "CIS 12"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]}
-known_false_positives = Very few legitimate Content-Type fields will have a length greater than 100 characters.
-providing_technologies = []
-
-[savedsearch://ESCU - W3WP Spawning Shell - Rule]
-type = detection
-asset_type =
-confidence = medium
-explanation = This query identifies a shell, PowerShell.exe or Cmd.exe, spawning from W3WP.exe, or IIS. In addition to IIS logs, this behavior with an EDR product will capture potential webshell activity, similar to the HAFNIUM Group abusing CVEs, on publicly available Exchange mail servers. During triage, review the parent process and child process of the shell being spawned. Review the command-line arguments and any file modifications that may occur. Identify additional parallel process, child processes, that may highlight further commands executed. After triaging, work to contain the threat and patch the system that is vulnerable.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
-annotations = {"kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1505.003"]}
-known_false_positives = Baseline your environment before production. It is possible build systems using IIS will spawn cmd.exe to perform a software build. Filter as needed.
-providing_technologies = []
-
-[savedsearch://ESCU - WBAdmin Delete System Backups - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for flags passed to wbadmin.exe (Windows Backup Administrator Tool) that delete backup files. This is typically used by ransomware to prevent recovery.
-how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. Tune based on parent process names.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1490"], "nist": ["PR.IP"]}
-known_false_positives = Administrators may modify the boot configuration.
-providing_technologies = []
-
-[savedsearch://ESCU - WMI Permanent Event Subscription - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for the creation of WMI permanent event subscriptions.
-how_to_implement = To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational].
-annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
-known_false_positives = Although unlikely, administrators may use event subscriptions for legitimate purposes.
-providing_technologies = []
-
-[savedsearch://ESCU - WMI Permanent Event Subscription - Sysmon - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for the creation of WMI permanent event subscriptions.
-how_to_implement = To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate alerts for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields.
-annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1546.003"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
-known_false_positives = Although unlikely, administrators may use event subscriptions for legitimate purposes.
-providing_technologies = []
-
-[savedsearch://ESCU - WMI Temporary Event Subscription - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for the creation of WMI temporary event subscriptions.
-how_to_implement = To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational].
-annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]}
-known_false_positives = Some software may create WMI temporary event subscriptions for various purposes. The included search contains an exception for two of these that occur by default on Windows 10 systems. You may need to modify the search to create exceptions for other legitimate events.
-providing_technologies = []
-
-[savedsearch://ESCU - Web Fraud - Account Harvesting - Rule]
-type = detection
-asset_type = Account
-confidence = medium
-explanation = This search is used to identify the creation of multiple user accounts using the same email domain name.
-how_to_implement = We start with a dataset that provides visibility into the email address used for the account creation. In this example, we are narrowing our search down to the single web page that hosts the Magento2 e-commerce platform (via URI) used for account creation, the single http content-type to grab only the user's clicks, and the http field that provides the username (form_data), for performance reasons. After we have the username and email domain, we look for numerous account creations per email domain. Common data sources used for this detection are customized Apache logs or Splunk Stream.
-annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136"], "nist": ["DE.CM", "DE.DP"]}
-known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamolous behavior. This search will need to be customized to fit your environment—improving its fidelity by counting based on something much more specific, such as a device ID that may be present in your dataset. Consideration for whether the large number of registrations are occuring from a first-time seen domain may also be important. Extending the search window to look further back in time, or even calculating the average per hour/day for each email domain to look for an anomalous spikes, will improve this search. You can also use Shannon entropy or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness or similarity of the email name or email domain, as the names are often machine-generated.
-providing_technologies = []
-
-[savedsearch://ESCU - Web Fraud - Anomalous User Clickspeed - Rule]
-type = detection
-asset_type = account
-confidence = medium
-explanation = This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session.
-how_to_implement = Start with a dataset that allows you to see clickstream data for each user click on the website. That data must have a time stamp and must contain a reference to the session identifier being used by the website. This ties the clicks together into clickstreams. This value is usually found in the http cookie. With a bit of tuning, a version of this search could be used in high-volume scenarios, such as scraping, crawling, application DDOS, credit-card testing, account takeover, etc. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream.
-annotations = {"cis20": ["CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "nist": ["DE.AE", "DE.CM"]}
-known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosly written detections that simply detect anamoluous behavior.
-providing_technologies = []
-
-[savedsearch://ESCU - Web Fraud - Password Sharing Across Accounts - Rule]
-type = detection
-asset_type = account
-confidence = medium
-explanation = This search is used to identify user accounts that share a common password.
-how_to_implement = We need to start with a dataset that allows us to see the values of usernames and passwords that users are submitting to the website hosting the Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized or hashed value of a password is acceptable and certainly preferable to a clear-text password. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream.
-annotations = {"cis20": ["CIS 16"], "nist": ["DE.DP"]}
-known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamoluous behavior.
-providing_technologies = []
-
-[savedsearch://ESCU - Web Servers Executing Suspicious Processes - Rule]
-type = detection
-asset_type = Web Server
-confidence = medium
-explanation = This search looks for suspicious processes on all systems labeled as web servers.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security.
-annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1082"], "nist": ["PR.IP"]}
-known_false_positives = Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks.
-providing_technologies = []
-
-[savedsearch://ESCU - Windows AdFind Exe - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for the execution of `adfind.exe` with command-line arguments that it uses by default. Specifically the filter or search functions. It also considers the arguments necessary like objectcategory, see readme for more details: https://www.joeware.net/freetools/tools/adfind/usage.htm. This has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST. AdFind.exe is usually used a recon tool to enumare a domain controller.
-how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1018"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = administrators rarely use adfind, usually not used for legitimate reasons
-providing_technologies = []
-
-[savedsearch://ESCU - Windows DisableAntiSpyware Registry - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The search looks for the Registry Key DisableAntiSpyware set to disable. This is consistent with Ryuk infections across a fleet of endpoints. This particular behavior is typically executed when an ransomware actor gains access to an endpoint and beings to perform execution. Usually, a batch (.bat) will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications. Endpoint should be isolated.
-how_to_implement = To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1562.001"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.
-providing_technologies = []
-
-[savedsearch://ESCU - Windows Event Log Cleared - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = This search looks for Windows events that indicate one of the Windows event logs has been purged.
-how_to_implement = To successfully implement this search, you need to be ingesting Windows event logs from your hosts.
-annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070.001"], "nist": ["DE.DP", "PR.IP", "PR.AC", "PR.AT", "DE.AE"]}
-known_false_positives = It is possible that these logs may be legitimately cleared by Administrators.
-providing_technologies = []
-
-[savedsearch://ESCU - Windows Security Account Manager Stopped - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The search looks for a Windows Security Account Manager (SAM) was stopped via command-line. This is consistent with Ryuk infections across a fleet of endpoints.
-how_to_implement = You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1489"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = SAM is a critical windows service, stopping it would cause major issues on an endpoint this makes false positive rare. AlthoughNo false positives have been identified.
-providing_technologies = []
-
-[savedsearch://ESCU - Windows connhost exe started forcefully - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The search looks for the Console Window Host process (connhost.exe) executed using the force flag -ForceV1. This is not regular behavior in the Windows OS and is often seen executed by the Ryuk Ransomware. DEPRECATED This event is actually seen in the windows 10 client of attack_range_local. After further testing we realized this is not specific to Ryuk.
-how_to_implement = You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.
-annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1059.003"], "nist": ["PR.PT", "DE.CM"]}
-known_false_positives = This process should not be ran forcefully, we have not see any false positives for this detection
-providing_technologies = []
-
-[savedsearch://ESCU - Windows hosts file modification - Rule]
-type = detection
-asset_type = Endpoint
-confidence = medium
-explanation = The search looks for modifications to the hosts file on all Windows endpoints across your environment.
-how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes.
-annotations = {"cis20": ["CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["PR.IP", "PR.PT", "PR.AC", "DE.AE", "DE.CM"]}
-known_false_positives = There may be legitimate reasons for system administrators to add entries to this file.
-providing_technologies = []
-
-[savedsearch://ESCU - aws detect attach to role policy - Rule]
-type = detection
-asset_type = AWS Account
-confidence = medium
-explanation = This search provides detection of an user attaching itself to a different role trust policy. This can be used for lateral movement and escalation of privileges.
-how_to_implement = You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs
-annotations = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
-known_false_positives = Attach to policy can create a lot of noise. This search can be adjusted to provide specific values to identify cases of abuse (i.e status=failure). The search can provide context for common users attaching themselves to higher privilege policies or even newly created policies.
-providing_technologies = []
-
-[savedsearch://ESCU - aws detect permanent key creation - Rule]
-type = detection
-asset_type = AWS Account
-confidence = medium
-explanation = This search provides detection of accounts creating permanent keys. Permanent keys are not created by default and they are only needed for programmatic calls. Creation of Permanent key is an important event to monitor.
-how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs
-annotations = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
-known_false_positives = Not all permanent key creations are malicious. If there is a policy of rotating keys this search can be adjusted to provide better context.
-providing_technologies = []
-
-[savedsearch://ESCU - aws detect role creation - Rule]
-type = detection
-asset_type = AWS Account
-confidence = medium
-explanation = This search provides detection of role creation by IAM users. Role creation is an event by itself if user is creating a new role with trust policies different than the available in AWS and it can be used for lateral movement and escalation of privileges.
-how_to_implement = You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs
-annotations = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
-known_false_positives = CreateRole is not very common in common users. This search can be adjusted to provide specific values to identify cases of abuse. In general AWS provides plenty of trust policies that fit most use cases.
-providing_technologies = []
-
-[savedsearch://ESCU - aws detect sts assume role abuse - Rule]
-type = detection
-asset_type = AWS Account
-confidence = medium
-explanation = This search provides detection of suspicious use of sts:AssumeRole. These tokens can be created on the go and used by attackers to move laterally and escalate privileges.
-how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs
-annotations = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
-known_false_positives = Sts:AssumeRole can be very noisy as it is a standard mechanism to provide cross account and cross resources access. This search can be adjusted to provide specific values to identify cases of abuse.
-providing_technologies = []
-
-[savedsearch://ESCU - aws detect sts get session token abuse - Rule]
-type = detection
-asset_type = AWS Account
-confidence = medium
-explanation = This search provides detection of suspicious use of sts:GetSessionToken. These tokens can be created on the go and used by attackers to move laterally and escalate privileges.
-how_to_implement = You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs
-annotations = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1550"]}
-known_false_positives = Sts:GetSessionToken can be very noisy as in certain environments numerous calls of this type can be executed. This search can be adjusted to provide specific values to identify cases of abuse. In specific environments the use of field requestParameters.serialNumber will need to be used.
-providing_technologies = []
-
-[savedsearch://ESCU - gcp detect oauth token abuse - Rule]
-type = detection
-asset_type = GCP Account
-confidence = medium
-explanation = This search provides detection of possible GCP Oauth token abuse. GCP Oauth token without time limit can be exfiltrated and reused for keeping access sessions alive without further control of authentication, allowing attackers to access and move laterally.
-how_to_implement = You must install splunk GCP add-on. This search works with gcp:pubsub:message logs
-annotations = {"kill_chain_phases": ["Lateral Movement"], "mitre_attack": ["T1078"]}
-known_false_positives = GCP Oauth token abuse detection will only work if there are access policies in place along with audit logs.
-providing_technologies = []
-
### END DETECTIONS ###
### RESPONSE TASKS ###
@@ -4887,14 +719,6 @@ known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0
-[savedsearch://ESCU - All backup logs for host - Response Task]
-type = investigation
-explanation = none
-how_to_implement = The successfully implement this search you must first send your backup logs to Splunk.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
[savedsearch://ESCU - Amazon EKS Kubernetes activity by src ip - Response Task]
type = investigation
explanation = none
@@ -4903,40 +727,6 @@ known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0
-[savedsearch://ESCU - DNS Hijack Enrichment - Response Task]
-type = investigation
-explanation = none
-how_to_implement = If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment" can be configured to run when any results are found by this detection search. The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal to detect if DNS issuers changed. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \
-(Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\
-
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Domain Certificate Investigation - Response Task]
-type = investigation
-explanation = none
-how_to_implement = To successfully implement this phantom playbook, you must integrate Enterprise Security with Phantom. Configure this playbook in the correlation search `Detect DNS requests to Phishing Sites leveraging EvilGinx2` ,as an adaptive response action.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Excessive Account Lockouts Enrichment And Response - Response Task]
-type = investigation
-explanation = none
-how_to_implement = Import playbook into phantom
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - GCP Kubernetes activity by src ip - Response Task]
-type = investigation
-explanation = none
-how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
[savedsearch://ESCU - Get All AWS Activity From City - Response Task]
type = investigation
explanation = none
@@ -4969,38 +759,6 @@ known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0
-[savedsearch://ESCU - Get Backup Logs For Endpoint - Response Task]
-type = investigation
-explanation = none
-how_to_implement = You must be ingesting your backup logs.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Get Certificate logs for a domain - Response Task]
-type = investigation
-explanation = none
-how_to_implement = You must be ingesting your certificates or SSL logs from your network traffic into your Certificates datamodel. Please note the wildcard(*) before domain in the search syntax, we use to match for all domain and subdomain combinations
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Get DNS Server History for a host - Response Task]
-type = investigation
-explanation = none
-how_to_implement = To successfully implement this search, you must be ingesting your DNS traffic
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Get DNS traffic ratio - Response Task]
-type = investigation
-explanation = none
-how_to_implement = You must be ingesting your network traffic
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
[savedsearch://ESCU - Get EC2 Instance Details by instanceId - Response Task]
type = investigation
explanation = none
@@ -5017,126 +775,6 @@ known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0
-[savedsearch://ESCU - Get Email Info - Response Task]
-type = investigation
-explanation = none
-how_to_implement = To successfully implement this search you must be ingesting your email logs or capturing unencrypted network traffic which contains email communications.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Get Emails From Specific Sender - Response Task]
-type = investigation
-explanation = none
-how_to_implement = To successfully implement this search you must ingest your email logs or capture unencrypted email communications within network traffic, and populate the Email data model.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Get First Occurrence and Last Occurrence of a MAC Address - Response Task]
-type = investigation
-explanation = none
-how_to_implement = To successfully implement this search, you must be ingesting the logs from your DHCP server.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Get History Of Email Sources - Response Task]
-type = investigation
-explanation = none
-how_to_implement = To successfully implement this search you must ingest your email logs or capture unencrypted email communications within network traffic, and populate the Email data model.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Get Logon Rights Modifications For Endpoint - Response Task]
-type = investigation
-explanation = none
-how_to_implement = To successfully implement this search you must be ingesting your Windows event logs
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Get Logon Rights Modifications For User - Response Task]
-type = investigation
-explanation = none
-how_to_implement = To successfully implement this search you must be ingesting your Windows event logs
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Get Notable History - Response Task]
-type = investigation
-explanation = none
-how_to_implement = If you are using Enterprise Security you are likely already creating notable events with your correlation rules. No additional configuration is necessary.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Get Outbound Emails to Hidden Cobra Threat Actors - Response Task]
-type = investigation
-explanation = none
-how_to_implement = To successfully implement this search you must ingest your email logs or capture unencrypted email communications within network traffic, and populate the Email data model.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Get Parent Process Info - Response Task]
-type = investigation
-explanation = none
-how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Get Process File Activity - Response Task]
-type = investigation
-explanation = none
-how_to_implement = To successfully implement this search you must be ingesting endpoint data and populating the Endpoint data model.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Get Process Info - Response Task]
-type = investigation
-explanation = none
-how_to_implement = To successfully implement this search you must be ingesting endpoint data and populating the Endpoint data model.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Get Process Information For Port Activity - Response Task]
-type = investigation
-explanation = none
-how_to_implement = To successfully implement this search you must be ingesting endpoint data that associates processes with network events and populate the Endpoint Datamodel
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Get Process Responsible For The DNS Traffic - Response Task]
-type = investigation
-explanation = none
-how_to_implement = You must be ingesting endpoint data that associates processes with network events into the Endpoint datamodel. This can come from endpoint protection products such as carbon black, or endpoint data sources such as Sysmon.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Get Sysmon WMI Activity for Host - Response Task]
-type = investigation
-explanation = none
-how_to_implement = To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate events for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Get Web Session Information via session id - Response Task]
-type = investigation
-explanation = none
-how_to_implement = This search leverages data extracted from Stream:HTTP. You must configure the HTTP stream using the Splunk Stream App on your Splunk Stream deployment server.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
[savedsearch://ESCU - Investigate AWS User Activities by user field - Response Task]
type = investigation
explanation = none
@@ -5153,94 +791,6 @@ known_false_positives = not defined
earliest_time_offset = 14400
latest_time_offset = 0
-[savedsearch://ESCU - Investigate Failed Logins for Multiple Destinations - Response Task]
-type = investigation
-explanation = none
-how_to_implement = To successfully implement this search you need to be ingesting authentication logs from your various systems and populating the Authentication data model.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Investigate Network Traffic From src ip - Response Task]
-type = investigation
-explanation = none
-how_to_implement = To successfully implement this search, you must be ingesting your web-traffic logs and populating the web data model.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Investigate Okta Activity by IP Address - Response Task]
-type = investigation
-explanation = none
-how_to_implement = You must be ingesting Okta logs
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Investigate Okta Activity by app - Response Task]
-type = investigation
-explanation = none
-how_to_implement = You must be ingesting Okta logs
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Investigate Pass the Hash Attempts - Response Task]
-type = investigation
-explanation = none
-how_to_implement = To successfully implement this search you need be ingesting windows security logs. This search uses an input macro named `wineventlog_security`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Security logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Investigate Pass the Ticket Attempts - Response Task]
-type = investigation
-explanation = none
-how_to_implement = To successfully implement this search you need to be ingesting windows security logs. This search uses an input macro named `wineventlog_security`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Security logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Investigate Previous Unseen User - Response Task]
-type = investigation
-explanation = none
-how_to_implement = To successfully implement this search you need to be ingesting authentication logs from your various systems and populating the Authentication data model.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Investigate Successful Remote Desktop Authentications - Response Task]
-type = investigation
-explanation = none
-how_to_implement = You must be populating the Authentication data model with security events from your Windows event logs.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Investigate Suspicious Strings in HTTP Header - Response Task]
-type = investigation
-explanation = none
-how_to_implement = This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Investigate User Activities In Okta - Response Task]
-type = investigation
-explanation = none
-how_to_implement = You must be ingesting Okta logs
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
-[savedsearch://ESCU - Investigate Web POSTs From src - Response Task]
-type = investigation
-explanation = none
-how_to_implement = To successfully implement this search, you must be ingesting your web-traffic logs and populating the web data model.
-known_false_positives = not defined
-earliest_time_offset = 14400
-latest_time_offset = 0
-
### END RESPONSE TASKS ###
### BASELINES ###
@@ -5274,157 +824,6 @@ how_to_implement = You must have Enterprise Security 6.0 or later, if not you wi
known_false_positives = not defined
providing_technologies = none
-[savedsearch://ESCU - Baseline of API Calls per User ARN]
-type = support
-explanation = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Baseline of Command Line Length - MLTK]
-type = support
-explanation = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the command lines observed for each user in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies outliers in the length of the command line.
-how_to_implement = You must be ingesting endpoint data and populating the Endpoint data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Baseline of DNS Query Length - MLTK]
-type = support
-explanation = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the DNS queries for each DNS record type observed in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which uses it to identify outliers in the length of the DNS query.
-how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK]
-type = support
-explanation = This search is used to build a Machine Learning Toolkit (MLTK) model for how many RunInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of RunInstances performed by a user in a small time window.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.\
-In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\
-More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK]
-type = support
-explanation = This search is used to build a Machine Learning Toolkit (MLTK) model for how many TerminateInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of TerminateInstances performed by a user in a small time window.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.\
-In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\
-More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Baseline of Network ACL Activity by ARN]
-type = support
-explanation = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls that were related to network ACLs made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for network ACLs, edit the macro `network_acl_events`.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Baseline of S3 Bucket deletion activity by ARN]
-type = support
-explanation = This search establishes, on a per-hour basis, the average and standard deviation for the number of API calls related to deleting an S3 bucket by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Baseline of SMB Traffic - MLTK]
-type = support
-explanation = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the number of SMB connections observed each hour for every day of week. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search to identify outliers in the number of SMB connections for that hour and day of the week.
-how_to_implement = You must be ingesting network traffic and populating the Network_Traffic data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. To improve your results, you may consider adding "src" to the by clause, which will build the model for each unique source in your enviornment. However, if you have a large number of hosts in your environment, this search may be very resource intensive. In this case, you may need to raise the value of max_inputs and/or max_groups in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Baseline of Security Group Activity by ARN]
-type = support
-explanation = This search establishes, on a per-hour basis, the average and the standard deviation for the number of API calls related to security groups made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for security groups, edit the macro `security_group_api_calls`.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Baseline of blocked outbound traffic from AWS]
-type = support
-explanation = This search establishes, on a per-hour basis, the average and the standard deviation of the number of outbound connections blocked in your VPC flow logs by each source IP address (IP address of your EC2 instances). Also recorded is the number of data points for each source IP. This table outputs to a lookup file to allow the detection search to operate quickly.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your `VPC flow logs.`.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Count of Unique IPs Connecting to Ports]
-type = support
-explanation = The search counts the number of times a connection was observed to each destination port, and the number of unique source IPs connecting to them.
-how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Count of assets by category]
-type = support
-explanation = This search shows you every asset category you have and the assets that belong to those categories.
-how_to_implement = To successfully implement this search you must first leverage the Assets and Identity framework in Enterprise Security to populate your assets_by_str.csv file which should then be mapped to the Identity_Management data model. The Identity_Management data model will contain a list of known authorized company assets. Ensure that all inventoried systems are constantly vetted and updated.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Create a list of approved AWS service accounts]
-type = support
-explanation = This search looks for successful API activity in CloudTrail within the last 30 days, filters out known users from the identity table, and outputs values of users into `aws_service_accounts.csv` lookup file.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the service account entires in `aws_service_accounts.csv`, which is a lookup file created as a result of running this support search. Please remove the entries of service accounts that are not legitimate.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - DNSTwist Domain Names]
-type = support
-explanation = This search creates permutations of your existing domains, removes the valid domain names and stores them in a specified lookup file so they can be checked for in the associated detection searches.
-how_to_implement = To successfully implement this search you need to update the file called domains.csv in the DA-ESS-SOC/lookup directory. Or `cim_corporate_email_domains.csv` and `cim_corporate_web_domains.csv` from **Splunk\_SA\_CIM**.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Discover DNS records]
-type = support
-explanation = The search takes corporate and common cloud provider domains configured under `cim_corporate_email_domains.csv`, `cim_corporate_web_domains.csv`, and `cloud_domains.csv` finds their responses across the last 30 days from data in the `Network_Resolution ` datamodel, then stores the output under the `discovered_dns_records.csv` lookup
-how_to_implement = To successfully implement this search, you must be ingesting DNS logs, and populating the Network_Resolution data model. Also make sure that the cim_corporate_web_domains and cim_corporate_email_domains lookups are populated with the domains owned by your corporation
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Identify Systems Creating Remote Desktop Traffic]
-type = support
-explanation = This search counts the numbers of times the system has generated remote desktop traffic.
-how_to_implement = To successfully implement this search, you must ingest network traffic and populate the Network_Traffic data model.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Identify Systems Receiving Remote Desktop Traffic]
-type = support
-explanation = This search counts the numbers of times the system has created remote desktop traffic
-how_to_implement = To successfully implement this search you must ingest network traffic and populate the Network_Traffic data model. If a system receives a lot of remote desktop traffic, you can apply the category common_rdp_destination to it.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Identify Systems Using Remote Desktop]
-type = support
-explanation = This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system.
-how_to_implement = To successfully implement this search you must be ingesting endpoint data that records process activity.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Monitor Successful Backups]
-type = support
-explanation = This search is intended to give you a feel for how often successful backups are conducted in your environment. Fluctuations in these numbers will allow you to determine when you should investigate.
-how_to_implement = To successfully implement this search you must be ingesting your backup logs.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Monitor Unsuccessful Backups]
-type = support
-explanation = This search is intended to give you a feel for how often backup failures happen in your environments. Fluctuations in these numbers will allow you to determine when you should investigate.
-how_to_implement = To successfully implement this search you must be ingesting your backup logs.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Previously Seen AWS Cross Account Activity]
-type = support
-explanation = This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this support search.
-known_false_positives = not defined
-providing_technologies = none
-
[savedsearch://ESCU - Previously Seen AWS Cross Account Activity - Initial]
type = support
explanation = This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file.
@@ -5439,20 +838,6 @@ how_to_implement = You must install and configure the Splunk Add-on for AWS (ver
known_false_positives = not defined
providing_technologies = none
-[savedsearch://ESCU - Previously Seen AWS Provisioning Activity Sources]
-type = support
-explanation = This search builds a table of the first and last times seen for every IP address (along with its physical location) previously associated with cloud-provisioning activity. This is broadly defined as any event that runs or creates something.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Previously Seen AWS Regions]
-type = support
-explanation = This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.
-known_false_positives = not defined
-providing_technologies = none
-
[savedsearch://ESCU - Previously Seen Cloud API Calls Per User Role - Initial]
type = support
explanation = This search builds a table of the first and last times seen for every user role and command combination. This is broadly defined as any event that runs or creates something. This table is then cached.
@@ -5551,48 +936,6 @@ how_to_implement = You must be ingesting the approrpiate cloud infrastructure lo
known_false_positives = not defined
providing_technologies = none
-[savedsearch://ESCU - Previously Seen EC2 AMIs]
-type = support
-explanation = This search builds a table of previously seen AMIs used to launch EC2 instances
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Previously Seen EC2 Instance Types]
-type = support
-explanation = This search builds a table of previously seen EC2 instance types
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Previously Seen EC2 Launches By User]
-type = support
-explanation = This search builds a table of previously seen ARNs that have launched a EC2 instance.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Previously Seen EC2 Modifications By User]
-type = support
-explanation = This search builds a table of previously seen ARNs that have launched a EC2 instance.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Previously Seen Running Windows Services - Initial]
-type = support
-explanation = This collects the services that have been started across your entire enterprise.
-how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs for it to execute successfully. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Previously Seen Running Windows Services - Update]
-type = support
-explanation = This search returns the first and last time a Windows service was seen across your enterprise within the last hour. It then updates this information with historical data and filters out Windows services pairs that have not been seen within the specified time window. This updated table is then cached.
-how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs for it to execute successfully. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above.
-known_false_positives = not defined
-providing_technologies = none
-
[savedsearch://ESCU - Previously Seen Users In CloudTrail - Update]
type = support
explanation = This search looks for CloudTrail events where a user logs into the console, then updates the baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by user, within the last hour.
@@ -5607,74 +950,4 @@ how_to_implement = You must install and configure the Splunk Add-on for AWS (ver
known_false_positives = not defined
providing_technologies = none
-[savedsearch://ESCU - Previously Seen Zoom Child Processes - Initial]
-type = support
-explanation = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS). This table is then cached.
-how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Previously Seen Zoom Child Processes - Update]
-type = support
-explanation = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS) within the last hour. It then updates this information with historical data and filters out proces_name and endpoint pairs that have not been seen within the specified time window. This updated table is outputed to disk.
-how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Previously seen API call per user roles in CloudTrail]
-type = support
-explanation = This search looks for successful API calls made by different user roles, then creates a baseline of the earliest and latest times we have encountered this user role. It also returns the name of the API call in our dataset--grouped by user role and name of the API call--that occurred within the last 30 days. In this support search, we are only looking for events where the user identity is Assumed Role.
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user role entries in `previously_seen_api_calls_from_user_roles.csv`, which is a lookup file created as a result of running this support search.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Previously seen S3 bucket access by remote IP]
-type = support
-explanation = This search looks for successful access to S3 buckets from remote IP addresses, then creates a baseline of the earliest and latest times we have encountered this remote IP within the last 30 days. In this support search, we are only looking for S3 access events where the HTTP response code from AWS is "200"
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access-logs inputs. You must validate the remote IP and bucket name entries in `previously_seen_S3_access_from_remote_ip.csv`, which is a lookup file created as a result of running this support search.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Previously seen command line arguments]
-type = support
-explanation = This search looks for command-line arguments where `cmd.exe /c` is used to execute a program, then creates a baseline of the earliest and latest times we have encountered this command-line argument in our dataset within the last 30 days.
-how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Previously seen users in CloudTrail]
-type = support
-explanation = This search looks for CloudTrail events where a user logs into the console, then creates a baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last 30 days. NOTE - This baseline search is deprecated and has been updated to use the Authentication Datamodel
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in `previously_seen_users_console_logins_cloudtrail`, which is a lookup file created as a result of running this support search.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Systems Ready for Spectre-Meltdown Windows Patch]
-type = support
-explanation = Some AV applications can cause the Spectre/Meltdown patch for Windows not to install successfully. This registry key is supposed to be created by the AV engine when it has been patched to be able to handle the Windows patch. If this key has been written, the system can then be patched for Spectre and Meltdown.
-how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Update previously seen users in CloudTrail]
-type = support
-explanation = This search looks for CloudTrail events where a user logs into the console, then updates the baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last hour. NOTE - This baseline search is deprecated and has been updated to use the Authentication Datamodel
-how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in `previously_seen_users_console_logins_cloudtrail`, which is a lookup file created as a result of running this support search.
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Windows Updates Install Failures]
-type = support
-explanation = This search is intended to give you a feel for how often Windows updates fail to install in your environment. Fluctuations in these numbers will allow you to determine when you should be concerned.
-how_to_implement = You must be ingesting your Windows Update Logs
-known_false_positives = not defined
-providing_technologies = none
-
-[savedsearch://ESCU - Windows Updates Install Successes]
-type = support
-explanation = This search is intended to give you a feel for how often successful Windows updates are applied in your environments. Fluctuations in these numbers will allow you to determine when you should be concerned.
-how_to_implement = You must be ingesting your Windows Update Logs
-known_false_positives = not defined
-providing_technologies = none
-
### END ESCU BASELINES ###
\ No newline at end of file
diff --git a/dist/saaws/default/app.conf b/dist/saaws/default/app.conf
index 2f76ac5958..3e490471a0 100644
--- a/dist/saaws/default/app.conf
+++ b/dist/saaws/default/app.conf
@@ -4,7 +4,7 @@
is_configured = false
state = enabled
state_change_requires_restart = false
-build = 25386
+build = 27110
[triggers]
reload.analytic_stories = simple
@@ -18,7 +18,7 @@ reload.content-version = simple
[launcher]
author = Splunk
-version = 3.18.0
+version = 3.19.0
description = Explore the Analytic Stories included with Splunk Security Analytics for AWS Content
[ui]
diff --git a/dist/saaws/default/collections.conf b/dist/saaws/default/collections.conf
index 1fd6a7f12a..7c0939e36e 100644
--- a/dist/saaws/default/collections.conf
+++ b/dist/saaws/default/collections.conf
@@ -1,6 +1,6 @@
#############
# Automatically generated by generator.py in splunk/security_content
-# On Date: 2021-04-12T22:03:03 UTC
+# On Date: 2021-04-22T21:35:56 UTC
# Author: Splunk Security Research
# Contact: research@splunk.com
#############
diff --git a/dist/saaws/default/content-version.conf b/dist/saaws/default/content-version.conf
index e45a39998e..8f448c9c05 100644
--- a/dist/saaws/default/content-version.conf
+++ b/dist/saaws/default/content-version.conf
@@ -1,2 +1,2 @@
[content-version]
-version = 3.18.0
+version = 3.19.0
diff --git a/dist/saaws/default/es_investigations.conf b/dist/saaws/default/es_investigations.conf
index 6b986f3dd3..ac83530284 100644
--- a/dist/saaws/default/es_investigations.conf
+++ b/dist/saaws/default/es_investigations.conf
@@ -11,7 +11,7 @@ label = AWS Network ACL Activity
description = Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it.
disabled = 0
-panels = ["panel://workbench_panel_aws_investigate_user_activities_by_arn___response_task", "panel://workbench_panel_aws_network_interface_details_via_resourceid___response_task", "panel://workbench_panel_get_all_aws_activity_from_ip_address___response_task", "panel://workbench_panel_aws_network_acl_details_from_id___response_task"]
+panels = ["panel://workbench_panel_get_all_aws_activity_from_ip_address___response_task", "panel://workbench_panel_aws_network_interface_details_via_resourceid___response_task", "panel://workbench_panel_aws_investigate_user_activities_by_arn___response_task", "panel://workbench_panel_aws_network_acl_details_from_id___response_task"]
[panel_group://workbench_panel_group_aws_security_hub_alerts]
label = AWS Security Hub Alerts
@@ -25,7 +25,7 @@ label = Cloud Cryptomining
description = Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior.
disabled = 0
-panels = ["panel://workbench_panel_get_ec2_instance_details_by_instanceid___response_task", "panel://workbench_panel_get_ec2_launch_details___response_task", "panel://workbench_panel_aws_investigate_user_activities_by_arn___response_task", "panel://workbench_panel_aws_investigate_security_hub_alerts_by_dest___response_task", "panel://workbench_panel_investigate_aws_activities_via_region_name___response_task"]
+panels = ["panel://workbench_panel_aws_investigate_security_hub_alerts_by_dest___response_task", "panel://workbench_panel_investigate_aws_activities_via_region_name___response_task", "panel://workbench_panel_get_ec2_launch_details___response_task", "panel://workbench_panel_get_ec2_instance_details_by_instanceid___response_task", "panel://workbench_panel_aws_investigate_user_activities_by_arn___response_task"]
[panel_group://workbench_panel_group_cloud_federated_credential_abuse]
label = Cloud Federated Credential Abuse
@@ -60,7 +60,7 @@ label = Suspicious AWS S3 Activities
description = Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required.
disabled = 0
-panels = ["panel://workbench_panel_aws_investigate_user_activities_by_arn___response_task", "panel://workbench_panel_aws_s3_bucket_details_via_bucketname___response_task", "panel://workbench_panel_get_all_aws_activity_from_ip_address___response_task", "panel://workbench_panel_investigate_aws_activities_via_region_name___response_task"]
+panels = ["panel://workbench_panel_get_all_aws_activity_from_ip_address___response_task", "panel://workbench_panel_aws_investigate_user_activities_by_arn___response_task", "panel://workbench_panel_investigate_aws_activities_via_region_name___response_task", "panel://workbench_panel_aws_s3_bucket_details_via_bucketname___response_task"]
[panel_group://workbench_panel_group_suspicious_cloud_authentication_activities]
label = Suspicious Cloud Authentication Activities
@@ -74,7 +74,7 @@ label = Suspicious Cloud Instance Activities
description = Monitor your cloud infrastructure provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your cloud environment.
disabled = 0
-panels = ["panel://workbench_panel_aws_investigate_user_activities_by_arn___response_task", "panel://workbench_panel_get_all_aws_activity_from_ip_address___response_task"]
+panels = ["panel://workbench_panel_get_all_aws_activity_from_ip_address___response_task", "panel://workbench_panel_aws_investigate_user_activities_by_arn___response_task"]
[panel_group://workbench_panel_group_suspicious_cloud_provisioning_activities]
label = Suspicious Cloud Provisioning Activities
diff --git a/dist/saaws/default/macros.conf b/dist/saaws/default/macros.conf
index 33c0f08703..d2a01fb00e 100644
--- a/dist/saaws/default/macros.conf
+++ b/dist/saaws/default/macros.conf
@@ -1,6 +1,6 @@
#############
# Automatically generated by generator.py in splunk/security_content
-# On Date: 2021-04-12T22:03:03 UTC
+# On Date: 2021-04-22T21:35:56 UTC
# Author: Splunk Security Research
# Contact: research@splunk.com
#############
@@ -315,6 +315,30 @@ description = Update this macro to limit the output results to filter out false
definition = search *
description = Update this macro to limit the output results to filter out false positives.
+[aws_excessive_security_scanning_filter]
+definition = search *
+description = Update this macro to limit the output results to filter out false positives.
+
+[aws_iam_accessdenied_discovery_events_filter]
+definition = search *
+description = Update this macro to limit the output results to filter out false positives.
+
+[aws_iam_assume_role_policy_brute_force_filter]
+definition = search *
+description = Update this macro to limit the output results to filter out false positives.
+
+[aws_iam_delete_policy_filter]
+definition = search *
+description = Update this macro to limit the output results to filter out false positives.
+
+[aws_iam_failure_group_deletion_filter]
+definition = search *
+description = Update this macro to limit the output results to filter out false positives.
+
+[aws_iam_successful_group_deletion_filter]
+definition = search *
+description = Update this macro to limit the output results to filter out false positives.
+
[aws_network_access_control_list_created_with_all_open_ports_filter]
definition = search *
description = Update this macro to limit the output results to filter out false positives.
diff --git a/dist/saaws/default/savedsearches.conf b/dist/saaws/default/savedsearches.conf
index 481be4bc2d..c545c2d734 100644
--- a/dist/saaws/default/savedsearches.conf
+++ b/dist/saaws/default/savedsearches.conf
@@ -1,6 +1,6 @@
#############
# Automatically generated by generator.py in splunk/security_content
-# On Date: 2021-04-12T22:03:03 UTC
+# On Date: 2021-04-22T21:35:56 UTC
# Author: Splunk Security Research
# Contact: research@splunk.com
#############
@@ -241,6 +241,215 @@ realtime_schedule = 0
is_visible = false
search = `cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms" | rename requestParameters.bucketName AS bucket_name, requestParameters.x-amz-copy-source AS src_file, requestParameters.key AS dest_file | stats count min(_time) as firstTime max(_time) as lastTime values(src_file) AS src_file values(dest_file) AS dest_file values(userAgent) AS userAgent values(region) AS region values(src) AS src by user | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` |`aws_detect_users_with_kms_keys_performing_encryption_s3_filter`
+[ESCU - AWS Excessive Security Scanning - Rule]
+action.escu = 0
+action.escu.enabled = 1
+description = This search looks for CloudTrail events and analyse the amount of eventNames which starts with Describe by a single user. This indicates that this user scans the configuration of your AWS cloud environment.
+action.escu.mappings = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1526"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
+action.escu.data_models = []
+action.escu.eli5 = This search looks for CloudTrail events and analyse the amount of eventNames which starts with Describe by a single user. This indicates that this user scans the configuration of your AWS cloud environment.
+action.escu.how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs.
+action.escu.known_false_positives = While this search has no known false positives.
+action.escu.creation_date = 2021-04-13
+action.escu.modification_date = 2021-04-13
+action.escu.confidence = high
+action.escu.full_search_name = ESCU - AWS Excessive Security Scanning - Rule
+action.escu.search_type = detection
+action.escu.product = ["Splunk Security Analytics for AWS", "Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud"]
+action.escu.providing_technologies = []
+action.escu.analytic_story = ["AWS User Monitoring"]
+action.risk = 1
+action.risk.param._risk_object = src
+action.risk.param._risk_object_type = system
+action.risk.param._risk_score = 20
+action.risk.param.verbose = 0
+cron_schedule = 0 * * * *
+dispatch.earliest_time = -70m@m
+dispatch.latest_time = -10m@m
+action.correlationsearch.enabled = 1
+action.correlationsearch.label = ESCU - AWS Excessive Security Scanning - Rule
+action.correlationsearch.annotations = {"analytic_story": ["AWS User Monitoring"], "cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1526"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
+schedule_window = auto
+alert.digest_mode = 1
+disabled = false
+enableSched = 1
+counttype = number of events
+relation = greater than
+quantity = 0
+realtime_schedule = 0
+is_visible = false
+search = `cloudtrail` eventName=Describe* OR eventName=List* OR eventName=Get* | stats dc(eventName) as dc_events min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName values(src) as src values(userAgent) as userAgent by user userIdentity.arn | where dc_events > 50 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`|`aws_excessive_security_scanning_filter`
+
+[ESCU - AWS IAM AccessDenied Discovery Events - Rule]
+action.escu = 0
+action.escu.enabled = 1
+description = The following detection identifies excessive AccessDenied events within an hour timeframe. It is possible that an access key to AWS may have been stolen and is being misused to perform discovery events. In these instances, the access is not available with the key stolen therefore these events will be generated.
+action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1580"]}
+action.escu.data_models = []
+action.escu.eli5 = The following detection identifies excessive AccessDenied events within an hour timeframe. It is possible that an access key to AWS may have been stolen and is being misused to perform discovery events. In these instances, the access is not available with the key stolen therefore these events will be generated.
+action.escu.how_to_implement = The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
+action.escu.known_false_positives = It is possible to start this detection will need to be tuned by source IP or user. In addition, change the count values to an upper threshold to restrict false positives.
+action.escu.creation_date = 2021-04-05
+action.escu.modification_date = 2021-04-05
+action.escu.confidence = high
+action.escu.full_search_name = ESCU - AWS IAM AccessDenied Discovery Events - Rule
+action.escu.search_type = detection
+action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud", "Splunk Security Analytics for AWS"]
+action.escu.providing_technologies = []
+action.escu.analytic_story = ["Suspicious Cloud User Activities"]
+cron_schedule = 0 * * * *
+dispatch.earliest_time = -70m@m
+dispatch.latest_time = -10m@m
+action.correlationsearch.enabled = 1
+action.correlationsearch.label = ESCU - AWS IAM AccessDenied Discovery Events - Rule
+action.correlationsearch.annotations = {"analytic_story": ["Suspicious Cloud User Activities"], "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1580"]}
+schedule_window = auto
+alert.digest_mode = 1
+disabled = false
+enableSched = 1
+counttype = number of events
+relation = greater than
+quantity = 0
+realtime_schedule = 0
+is_visible = false
+search = `cloudtrail` (errorCode = "AccessDenied") user_type=IAMUser (userAgent!=*.amazonaws.com) | bucket _time span=1h | stats count as failures min(_time) as firstTime max(_time) as lastTime, dc(eventName) as methods, dc(eventSource) as sources values(userIdentity.arn) by src_ip, userIdentity.arn, _time | where failures >= 5 and methods >= 1 and sources >= 1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_accessdenied_discovery_events_filter`
+
+[ESCU - AWS IAM Assume Role Policy Brute Force - Rule]
+action.escu = 0
+action.escu.enabled = 1
+description = The following detection identifies any malformed policy document exceptions with a status of `failure`. A malformed policy document exception occurs in instances where roles are attempted to be assumed, or brute forced. In a brute force attempt, using a tool like CloudSploit or Pacu, an attempt will look like `arn:aws:iam::111111111111:role/aws-service-role/rds.amazonaws.com/AWSServiceRoleForRDS`. Meaning, when an adversary is attempting to identify a role name, multiple failures will occur. This detection focuses on the errors of a remote attempt that is failing.
+action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1580", "T1110"]}
+action.escu.data_models = []
+action.escu.eli5 = The following detection identifies any malformed policy document exceptions with a status of `failure`. A malformed policy document exception occurs in instances where roles are attempted to be assumed, or brute forced. In a brute force attempt, using a tool like CloudSploit or Pacu, an attempt will look like `arn:aws:iam::111111111111:role/aws-service-role/rds.amazonaws.com/AWSServiceRoleForRDS`. Meaning, when an adversary is attempting to identify a role name, multiple failures will occur. This detection focuses on the errors of a remote attempt that is failing.
+action.escu.how_to_implement = The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs. Set the `where count` greater than a value to identify suspicious activity in your environment.
+action.escu.known_false_positives = This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users.
+action.escu.creation_date = 2021-04-01
+action.escu.modification_date = 2021-04-01
+action.escu.confidence = high
+action.escu.full_search_name = ESCU - AWS IAM Assume Role Policy Brute Force - Rule
+action.escu.search_type = detection
+action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud", "Splunk Security Analytics for AWS"]
+action.escu.providing_technologies = []
+action.escu.analytic_story = ["AWS IAM Privilege Escalation"]
+cron_schedule = 0 * * * *
+dispatch.earliest_time = -70m@m
+dispatch.latest_time = -10m@m
+action.correlationsearch.enabled = 1
+action.correlationsearch.label = ESCU - AWS IAM Assume Role Policy Brute Force - Rule
+action.correlationsearch.annotations = {"analytic_story": ["AWS IAM Privilege Escalation"], "kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1580", "T1110"]}
+schedule_window = auto
+alert.digest_mode = 1
+disabled = false
+enableSched = 1
+counttype = number of events
+relation = greater than
+quantity = 0
+realtime_schedule = 0
+is_visible = false
+search = `cloudtrail` (errorCode=MalformedPolicyDocumentException) status=failure (userAgent!=*.amazonaws.com) | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyName) as policy_name by src eventName eventSource aws_account_id errorCode requestParameters.policyDocument userAgent eventID awsRegion userIdentity.principalId user_arn | where count >= 2 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_assume_role_policy_brute_force_filter`
+
+[ESCU - AWS IAM Delete Policy - Rule]
+action.escu = 0
+action.escu.enabled = 1
+description = The following detection identifes when a policy is deleted on AWS. This does not identify whether successful or failed, but the error messages tell a story of suspicious attempts. There is a specific process to follow when deleting a policy. First, detach the policy from all users, groups, and roles that the policy is attached to, using DetachUserPolicy , DetachGroupPolicy , or DetachRolePolicy.
+action.escu.mappings = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1098"]}
+action.escu.data_models = []
+action.escu.eli5 = The following detection identifes when a policy is deleted on AWS. This does not identify whether successful or failed, but the error messages tell a story of suspicious attempts. There is a specific process to follow when deleting a policy. First, detach the policy from all users, groups, and roles that the policy is attached to, using DetachUserPolicy , DetachGroupPolicy , or DetachRolePolicy.
+action.escu.how_to_implement = The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
+action.escu.known_false_positives = This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete policies (least privilege). In addition, this may be saved seperately and tuned for failed or success attempts only.
+action.escu.creation_date = 2021-04-01
+action.escu.modification_date = 2021-04-01
+action.escu.confidence = high
+action.escu.full_search_name = ESCU - AWS IAM Delete Policy - Rule
+action.escu.search_type = detection
+action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud", "Splunk Security Analytics for AWS"]
+action.escu.providing_technologies = []
+action.escu.analytic_story = ["AWS IAM Privilege Escalation"]
+cron_schedule = 0 * * * *
+dispatch.earliest_time = -70m@m
+dispatch.latest_time = -10m@m
+action.correlationsearch.enabled = 1
+action.correlationsearch.label = ESCU - AWS IAM Delete Policy - Rule
+action.correlationsearch.annotations = {"analytic_story": ["AWS IAM Privilege Escalation"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1098"]}
+schedule_window = auto
+alert.digest_mode = 1
+disabled = false
+enableSched = 1
+counttype = number of events
+relation = greater than
+quantity = 0
+realtime_schedule = 0
+is_visible = false
+search = `cloudtrail` eventName=DeletePolicy (userAgent!=*.amazonaws.com) | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyArn) as policyArn by src eventName eventSource aws_account_id errorCode errorMessage userAgent eventID awsRegion userIdentity.principalId userIdentity.arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_delete_policy_filter`
+
+[ESCU - AWS IAM Failure Group Deletion - Rule]
+action.escu = 0
+action.escu.enabled = 1
+description = This detection identifies failure attempts to delete groups. We want to identify when a group is attempting to be deleted, but either access is denied, there is a conflict or there is no group. This is indicative of administrators performing an action, but also could be suspicious behavior occurring. Review parallel IAM events - recently added users, new groups and so forth.
+action.escu.mappings = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1098"]}
+action.escu.data_models = []
+action.escu.eli5 = This detection identifies failure attempts to delete groups. We want to identify when a group is attempting to be deleted, but either access is denied, there is a conflict or there is no group. This is indicative of administrators performing an action, but also could be suspicious behavior occurring. Review parallel IAM events - recently added users, new groups and so forth.
+action.escu.how_to_implement = The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
+action.escu.known_false_positives = This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege).
+action.escu.creation_date = 2021-04-01
+action.escu.modification_date = 2021-04-01
+action.escu.confidence = high
+action.escu.full_search_name = ESCU - AWS IAM Failure Group Deletion - Rule
+action.escu.search_type = detection
+action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud", "Splunk Security Analytics for AWS"]
+action.escu.providing_technologies = []
+action.escu.analytic_story = ["AWS IAM Privilege Escalation"]
+cron_schedule = 0 * * * *
+dispatch.earliest_time = -70m@m
+dispatch.latest_time = -10m@m
+action.correlationsearch.enabled = 1
+action.correlationsearch.label = ESCU - AWS IAM Failure Group Deletion - Rule
+action.correlationsearch.annotations = {"analytic_story": ["AWS IAM Privilege Escalation"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1098"]}
+schedule_window = auto
+alert.digest_mode = 1
+disabled = false
+enableSched = 1
+counttype = number of events
+relation = greater than
+quantity = 0
+realtime_schedule = 0
+is_visible = false
+search = `cloudtrail` eventSource=iam.amazonaws.com eventName=DeleteGroup errorCode IN (NoSuchEntityException,DeleteConflictException, AccessDenied) (userAgent!=*.amazonaws.com) | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.groupName) as group_name by src eventName eventSource aws_account_id errorCode errorMessage userAgent eventID awsRegion userIdentity.principalId user_arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_failure_group_deletion_filter`
+
+[ESCU - AWS IAM Successful Group Deletion - Rule]
+action.escu = 0
+action.escu.enabled = 1
+description = The following query uses IAM events to track the success of a group being deleted on AWS. This is typically not indicative of malicious behavior, but a precurser to additional events thay may unfold. Review parallel IAM events - recently added users, new groups and so forth. Inversely, review failed attempts in a similar manner.
+action.escu.mappings = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1069.003", "T1098"]}
+action.escu.data_models = []
+action.escu.eli5 = The following query uses IAM events to track the success of a group being deleted on AWS. This is typically not indicative of malicious behavior, but a precurser to additional events thay may unfold. Review parallel IAM events - recently added users, new groups and so forth. Inversely, review failed attempts in a similar manner.
+action.escu.how_to_implement = The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
+action.escu.known_false_positives = This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege).
+action.escu.creation_date = 2021-03-31
+action.escu.modification_date = 2021-03-31
+action.escu.confidence = high
+action.escu.full_search_name = ESCU - AWS IAM Successful Group Deletion - Rule
+action.escu.search_type = detection
+action.escu.product = ["Splunk Enterprise", "Splunk Enterprise Security", "Splunk Cloud", "Splunk Security Analytics for AWS"]
+action.escu.providing_technologies = []
+action.escu.analytic_story = ["AWS IAM Privilege Escalation"]
+cron_schedule = 0 * * * *
+dispatch.earliest_time = -70m@m
+dispatch.latest_time = -10m@m
+action.correlationsearch.enabled = 1
+action.correlationsearch.label = ESCU - AWS IAM Successful Group Deletion - Rule
+action.correlationsearch.annotations = {"analytic_story": ["AWS IAM Privilege Escalation"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1069.003", "T1098"]}
+schedule_window = auto
+alert.digest_mode = 1
+disabled = false
+enableSched = 1
+counttype = number of events
+relation = greater than
+quantity = 0
+realtime_schedule = 0
+is_visible = false
+search = `cloudtrail` eventSource=iam.amazonaws.com eventName=DeleteGroup errorCode=success (userAgent!=*.amazonaws.com) | stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.groupName) by src eventName eventSource errorCode user_agent awsRegion userIdentity.principalId user_arn | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `aws_iam_successful_group_deletion_filter`
+
[ESCU - AWS Network Access Control List Created with All Open Ports - Rule]
action.escu = 0
action.escu.enabled = 1
diff --git a/dist/saaws/default/transforms.conf b/dist/saaws/default/transforms.conf
index 5ece708b26..9e663f5fae 100644
--- a/dist/saaws/default/transforms.conf
+++ b/dist/saaws/default/transforms.conf
@@ -1,6 +1,6 @@
#############
# Automatically generated by generator.py in splunk/security_content
-# On Date: 2021-04-12T22:03:03 UTC
+# On Date: 2021-04-22T21:35:56 UTC
# Author: Splunk Security Research
# Contact: research@splunk.com
#############
diff --git a/dist/saaws/default/use_case_library.conf b/dist/saaws/default/use_case_library.conf
index f003970623..57aedaa6d7 100644
--- a/dist/saaws/default/use_case_library.conf
+++ b/dist/saaws/default/use_case_library.conf
@@ -1,6 +1,6 @@
#############
# Automatically generated by generator.py in splunk/security_content
-# On Date: 2021-04-12T22:03:03 UTC
+# On Date: 2021-04-22T21:35:56 UTC
# Author: Splunk Security Research
# Contact: research@splunk.com
#############
@@ -14,7 +14,7 @@ version = 1
references = ["https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation/", "https://www.cyberark.com/resources/threat-research-blog/the-cloud-shadow-admin-threat-10-permissions-to-protect", "https://labs.bishopfox.com/tech-blog/privilege-escalation-in-aws"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
-searches = ["ESCU - AWS Create Policy Version to allow all resources - Rule", "ESCU - AWS UpdateLoginProfile - Rule", "ESCU - AWS SetDefaultPolicyVersion - Rule", "ESCU - AWS CreateAccessKey - Rule", "ESCU - AWS CreateLoginProfile - Rule"]
+searches = ["ESCU - AWS IAM Failure Group Deletion - Rule", "ESCU - AWS CreateLoginProfile - Rule", "ESCU - AWS Create Policy Version to allow all resources - Rule", "ESCU - AWS IAM Delete Policy - Rule", "ESCU - AWS SetDefaultPolicyVersion - Rule", "ESCU - AWS UpdateLoginProfile - Rule", "ESCU - AWS IAM Successful Group Deletion - Rule", "ESCU - AWS IAM Assume Role Policy Brute Force - Rule", "ESCU - AWS CreateAccessKey - Rule"]
description = This analytic story contains detections that query your AWS Cloudtrail for activities related to privilege escalation.
narrative = Amazon Web Services provides a neat feature called Identity and Access Management (IAM) that enables organizations to manage various AWS services and resources in a secure way. All IAM users have roles, groups and policies associated with them which governs and sets permissions to allow a user to access specific restrictions.\
However, if these IAM policies are misconfigured and have specific combinations of weak permissions; it can allow attackers to escalate their privileges and further compromise the organization. Rhino Security Labs have published comprehensive blogs detailing various AWS Escalation methods. By using this as an inspiration, Splunk’s research team wants to highlight how these attack vectors look in AWS Cloudtrail logs and provide you with detection queries to uncover these potentially malicious events via this Analytic Story. \
@@ -26,7 +26,7 @@ version = 2
references = ["https://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Appendix_NACLs.html", "https://aws.amazon.com/blogs/security/how-to-help-prepare-for-ddos-attacks-by-reducing-your-attack-surface/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
-searches = ["ESCU - AWS Network Access Control List Created with All Open Ports - Rule", "ESCU - AWS Network Access Control List Deleted - Rule", "ESCU - AWS Investigate User Activities By ARN - Response Task", "ESCU - AWS Network Interface details via resourceId - Response Task", "ESCU - Get All AWS Activity From IP Address - Response Task", "ESCU - AWS Network ACL Details from ID - Response Task"]
+searches = ["ESCU - AWS Network Access Control List Deleted - Rule", "ESCU - AWS Network Access Control List Created with All Open Ports - Rule", "ESCU - Get All AWS Activity From IP Address - Response Task", "ESCU - AWS Network Interface details via resourceId - Response Task", "ESCU - AWS Investigate User Activities By ARN - Response Task", "ESCU - AWS Network ACL Details from ID - Response Task"]
description = Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it.
narrative = AWS CloudTrail is an AWS service that helps you enable governance, compliance, and operational/risk auditing of your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail. It is crucial for a company to monitor events and actions taken in the AWS Management Console, AWS Command Line Interface, and AWS SDKs and APIs to ensure that your servers are not vulnerable to attacks. This analytic story contains detection searches that leverage CloudTrail logs from AWS to check for bad configurations and malicious activity in your AWS network access controls.
@@ -48,7 +48,7 @@ version = 1
references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
-searches = ["ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule", "ESCU - Cloud Compute Instance Created In Previously Unused Region - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule", "ESCU - Abnormally High Number Of Cloud Instances Launched - Rule", "ESCU - Get EC2 Instance Details by instanceId - Response Task", "ESCU - Get EC2 Launch Details - Response Task", "ESCU - AWS Investigate User Activities By ARN - Response Task", "ESCU - AWS Investigate Security Hub alerts by dest - Response Task", "ESCU - Investigate AWS activities via region name - Response Task"]
+searches = ["ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule", "ESCU - Cloud Compute Instance Created In Previously Unused Region - Rule", "ESCU - Abnormally High Number Of Cloud Instances Launched - Rule", "ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule", "ESCU - AWS Investigate Security Hub alerts by dest - Response Task", "ESCU - Investigate AWS activities via region name - Response Task", "ESCU - Get EC2 Launch Details - Response Task", "ESCU - Get EC2 Instance Details by instanceId - Response Task", "ESCU - AWS Investigate User Activities By ARN - Response Task"]
description = Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior.
narrative = Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \
Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Azure. It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \
@@ -62,7 +62,7 @@ version = 1
references = ["https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps", "https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf", "https://us-cert.cisa.gov/ncas/alerts/aa21-008a"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rod Soto"}]
spec_version = 3
-searches = ["ESCU - O365 Excessive SSO logon errors - Rule", "ESCU - AWS SAML Update identity provider - Rule", "ESCU - O365 New Federated Domain Added - Rule", "ESCU - O365 Add App Role Assignment Grant User - Rule", "ESCU - O365 Added Service Principal - Rule", "ESCU - AWS SAML Access by Provider User and Principal - Rule"]
+searches = ["ESCU - O365 Excessive SSO logon errors - Rule", "ESCU - O365 Add App Role Assignment Grant User - Rule", "ESCU - AWS SAML Access by Provider User and Principal - Rule", "ESCU - O365 Added Service Principal - Rule", "ESCU - AWS SAML Update identity provider - Rule", "ESCU - O365 New Federated Domain Added - Rule"]
description = This analytical story addresses events that indicate abuse of cloud federated credentials. These credentials are usually extracted from endpoint desktop or servers specially those servers that provide federation services such as Windows Active Directory Federation Services. Identity Federation relies on objects such as Oauth2 tokens, cookies or SAML assertions in order to provide seamless access between cloud and perimeter environments. If these objects are either hijacked or forged then attackers will be able to pivot into victim's cloud environements.
narrative = This story is composed of detection searches based on endpoint that addresses the use of Mimikatz, Escalation of Privileges and Abnormal processes that may indicate the extraction of Federated directory objects such as passwords, Oauth2 tokens, certificates and keys. Cloud environment (AWS, Azure) related events are also addressed in specific cloud environment detection searches.
@@ -73,7 +73,7 @@ version = 1
references = ["https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Patrick Bareiss"}]
spec_version = 3
-searches = ["ESCU - O365 Disable MFA - Rule", "ESCU - O365 Excessive SSO logon errors - Rule", "ESCU - O365 Excessive Authentication Failures Alert - Rule", "ESCU - O365 Suspicious Admin Email Forwarding - Rule", "ESCU - O365 Bypass MFA via Trusted IP - Rule", "ESCU - O365 PST export alert - Rule", "ESCU - O365 Suspicious Rights Delegation - Rule", "ESCU - O365 New Federated Domain Added - Rule", "ESCU - O365 Add App Role Assignment Grant User - Rule", "ESCU - O365 Suspicious User Email Forwarding - Rule", "ESCU - O365 Added Service Principal - Rule"]
+searches = ["ESCU - O365 Excessive SSO logon errors - Rule", "ESCU - O365 Suspicious Rights Delegation - Rule", "ESCU - O365 Add App Role Assignment Grant User - Rule", "ESCU - O365 PST export alert - Rule", "ESCU - O365 Excessive Authentication Failures Alert - Rule", "ESCU - O365 Bypass MFA via Trusted IP - Rule", "ESCU - O365 Disable MFA - Rule", "ESCU - O365 Added Service Principal - Rule", "ESCU - O365 Suspicious User Email Forwarding - Rule", "ESCU - O365 New Federated Domain Added - Rule", "ESCU - O365 Suspicious Admin Email Forwarding - Rule"]
description = This story is focused around detecting Office 365 Attacks.
narrative = More and more companies are using Microsofts Office 365 cloud offering. Therefore, we see more and more attacks against Office 365. This story provides various detections for Office 365 attacks.
@@ -95,7 +95,7 @@ version = 1
references = ["https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
-searches = ["ESCU - Detect AWS Console Login by User from New City - Rule", "ESCU - Detect AWS Console Login by User from New Region - Rule", "ESCU - Detect AWS Console Login by User from New Country - Rule", "ESCU - AWS Investigate User Activities By ARN - Response Task"]
+searches = ["ESCU - Detect AWS Console Login by User from New Region - Rule", "ESCU - Detect AWS Console Login by User from New City - Rule", "ESCU - Detect AWS Console Login by User from New Country - Rule", "ESCU - AWS Investigate User Activities By ARN - Response Task"]
description = Monitor your AWS authentication events using your CloudTrail logs. Searches within this Analytic Story will help you stay aware of and investigate suspicious logins.
narrative = It is important to monitor and control who has access to your AWS infrastructure. Detecting suspicious logins to your AWS infrastructure will provide good starting points for investigations. Abusive behaviors caused by compromised credentials can lead to direct monetary costs, as you will be billed for any EC2 instances created by the attacker.
@@ -106,7 +106,7 @@ version = 2
references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://www.tripwire.com/state-of-security/security-data-protection/cloud/public-aws-s3-buckets-writable/"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}]
spec_version = 3
-searches = ["ESCU - Detect New Open S3 buckets - Rule", "ESCU - Detect New Open S3 Buckets over AWS CLI - Rule", "ESCU - AWS Investigate User Activities By ARN - Response Task", "ESCU - AWS S3 Bucket details via bucketName - Response Task", "ESCU - Get All AWS Activity From IP Address - Response Task", "ESCU - Investigate AWS activities via region name - Response Task"]
+searches = ["ESCU - Detect New Open S3 Buckets over AWS CLI - Rule", "ESCU - Detect New Open S3 buckets - Rule", "ESCU - Get All AWS Activity From IP Address - Response Task", "ESCU - AWS Investigate User Activities By ARN - Response Task", "ESCU - Investigate AWS activities via region name - Response Task", "ESCU - AWS S3 Bucket details via bucketName - Response Task"]
description = Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required.
narrative = As cloud computing has exploded, so has the number of creative attacks on virtual environments. And as the number-two cloud-service provider, Amazon Web Services (AWS) has certainly had its share.\
Amazon's "shared responsibility" model dictates that the company has responsibility for the environment outside of the VM and the customer is responsible for the security inside of the S3 container. As such, it's important to stay vigilant for activities that may belie suspicious behavior inside of your environment.\
@@ -119,7 +119,7 @@ version = 1
references = ["https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/", "https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html"]
maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}]
spec_version = 3
-searches = ["ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule", "ESCU - Detect AWS Console Login by User from New Region - Rule", "ESCU - Detect AWS Console Login by User from New City - Rule", "ESCU - Detect AWS Console Login by User from New Country - Rule", "ESCU - Detect AWS Console Login by New User - Rule", "ESCU - Investigate AWS User Activities by user field - Response Task"]
+searches = ["ESCU - Detect AWS Console Login by User from New Country - Rule", "ESCU - Detect AWS Console Login by User from New Region - Rule", "ESCU - Detect AWS Console Login by User from New City - Rule", "ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule", "ESCU - Detect AWS Console Login by New User - Rule", "ESCU - Investigate AWS User Activities by user field - Response Task"]
description = Monitor your cloud authentication events. Searches within this Analytic Story leverage the recent cloud updates to the Authentication data model to help you stay aware of and investigate suspicious login activity.
narrative = It is important to monitor and control who has access to your cloud infrastructure. Detecting suspicious logins will provide good starting points for investigations. Abusive behaviors caused by compromised credentials can lead to direct monetary costs, as you will be billed for any compute activity whether legitimate or otherwise.\
This Analytic Story has data model versions of cloud searches leveraging Authentication data, including those looking for suspicious login activity, and cross-account activity for AWS.
@@ -131,7 +131,7 @@ version = 1
references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
-searches = ["ESCU - Abnormally High Number Of Cloud Instances Launched - Rule", "ESCU - Cloud Instance Modified By Previously Unseen User - Rule", "ESCU - Abnormally High Number Of Cloud Instances Destroyed - Rule", "ESCU - AWS Investigate User Activities By ARN - Response Task", "ESCU - Get All AWS Activity From IP Address - Response Task"]
+searches = ["ESCU - Abnormally High Number Of Cloud Instances Launched - Rule", "ESCU - Abnormally High Number Of Cloud Instances Destroyed - Rule", "ESCU - Cloud Instance Modified By Previously Unseen User - Rule", "ESCU - Get All AWS Activity From IP Address - Response Task", "ESCU - AWS Investigate User Activities By ARN - Response Task"]
description = Monitor your cloud infrastructure provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your cloud environment.
narrative = Monitoring your cloud infrastructure logs allows you enable governance, compliance, and risk auditing. It is crucial for a company to monitor events and actions taken in the their cloud environments to ensure that your instances are not vulnerable to attacks. This Analytic Story identifies suspicious activities in your cloud compute instances and helps you respond and investigate those activities.
@@ -154,7 +154,7 @@ version = 1
references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://redlock.io/blog/cryptojacking-tesla"]
maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}]
spec_version = 3
-searches = ["ESCU - Abnormally High Number Of Cloud Infrastructure API Calls - Rule", "ESCU - Cloud API Calls From Previously Unseen User Roles - Rule", "ESCU - Abnormally High Number Of Cloud Security Group API Calls - Rule", "ESCU - AWS Investigate User Activities By ARN - Response Task"]
+searches = ["ESCU - AWS IAM AccessDenied Discovery Events - Rule", "ESCU - Abnormally High Number Of Cloud Infrastructure API Calls - Rule", "ESCU - Cloud API Calls From Previously Unseen User Roles - Rule", "ESCU - Abnormally High Number Of Cloud Security Group API Calls - Rule", "ESCU - AWS Investigate User Activities By ARN - Response Task"]
description = Detect and investigate suspicious activities by users and roles in your cloud environments.
narrative = It seems obvious that it is critical to monitor and control the users who have access to your cloud infrastructure. Nevertheless, it's all too common for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable to attack. In fact, this was the very oversight that led to Tesla's cryptojacking attack in February, 2018.\
In addition to compromising the security of your data, when bad actors leverage your compute resources, it can incur monumental costs, since you will be billed for any new instances and increased bandwidth usage.
@@ -223,6 +223,66 @@ annotations = {"mitre_attack": ["T1486"]}
known_false_positives = bucket with S3 encryption
providing_technologies = []
+[savedsearch://ESCU - AWS Excessive Security Scanning - Rule]
+type = detection
+asset_type = AWS Account
+confidence = medium
+explanation = This search looks for CloudTrail events and analyse the amount of eventNames which starts with Describe by a single user. This indicates that this user scans the configuration of your AWS cloud environment.
+how_to_implement = You must install splunk AWS add on and Splunk App for AWS. This search works with cloudtrail logs.
+annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1526"], "nist": ["PR.DS", "PR.AC", "DE.CM"]}
+known_false_positives = While this search has no known false positives.
+providing_technologies = []
+
+[savedsearch://ESCU - AWS IAM AccessDenied Discovery Events - Rule]
+type = detection
+asset_type =
+confidence = medium
+explanation = The following detection identifies excessive AccessDenied events within an hour timeframe. It is possible that an access key to AWS may have been stolen and is being misused to perform discovery events. In these instances, the access is not available with the key stolen therefore these events will be generated.
+how_to_implement = The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
+annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1580"]}
+known_false_positives = It is possible to start this detection will need to be tuned by source IP or user. In addition, change the count values to an upper threshold to restrict false positives.
+providing_technologies = []
+
+[savedsearch://ESCU - AWS IAM Assume Role Policy Brute Force - Rule]
+type = detection
+asset_type =
+confidence = medium
+explanation = The following detection identifies any malformed policy document exceptions with a status of `failure`. A malformed policy document exception occurs in instances where roles are attempted to be assumed, or brute forced. In a brute force attempt, using a tool like CloudSploit or Pacu, an attempt will look like `arn:aws:iam::111111111111:role/aws-service-role/rds.amazonaws.com/AWSServiceRoleForRDS`. Meaning, when an adversary is attempting to identify a role name, multiple failures will occur. This detection focuses on the errors of a remote attempt that is failing.
+how_to_implement = The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs. Set the `where count` greater than a value to identify suspicious activity in your environment.
+annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1580", "T1110"]}
+known_false_positives = This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users.
+providing_technologies = []
+
+[savedsearch://ESCU - AWS IAM Delete Policy - Rule]
+type = detection
+asset_type =
+confidence = medium
+explanation = The following detection identifes when a policy is deleted on AWS. This does not identify whether successful or failed, but the error messages tell a story of suspicious attempts. There is a specific process to follow when deleting a policy. First, detach the policy from all users, groups, and roles that the policy is attached to, using DetachUserPolicy , DetachGroupPolicy , or DetachRolePolicy.
+how_to_implement = The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
+annotations = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1098"]}
+known_false_positives = This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete policies (least privilege). In addition, this may be saved seperately and tuned for failed or success attempts only.
+providing_technologies = []
+
+[savedsearch://ESCU - AWS IAM Failure Group Deletion - Rule]
+type = detection
+asset_type =
+confidence = medium
+explanation = This detection identifies failure attempts to delete groups. We want to identify when a group is attempting to be deleted, but either access is denied, there is a conflict or there is no group. This is indicative of administrators performing an action, but also could be suspicious behavior occurring. Review parallel IAM events - recently added users, new groups and so forth.
+how_to_implement = The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
+annotations = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1098"]}
+known_false_positives = This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege).
+providing_technologies = []
+
+[savedsearch://ESCU - AWS IAM Successful Group Deletion - Rule]
+type = detection
+asset_type =
+confidence = medium
+explanation = The following query uses IAM events to track the success of a group being deleted on AWS. This is typically not indicative of malicious behavior, but a precurser to additional events thay may unfold. Review parallel IAM events - recently added users, new groups and so forth. Inversely, review failed attempts in a similar manner.
+how_to_implement = The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
+annotations = {"kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1069.003", "T1098"]}
+known_false_positives = This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege).
+providing_technologies = []
+
[savedsearch://ESCU - AWS Network Access Control List Created with All Open Ports - Rule]
type = detection
asset_type = AWS Instance
diff --git a/dist/saaws/lookups/mitre_enrichment.csv b/dist/saaws/lookups/mitre_enrichment.csv
index 49fac5e18b..0717cbc6ba 100644
--- a/dist/saaws/lookups/mitre_enrichment.csv
+++ b/dist/saaws/lookups/mitre_enrichment.csv
@@ -1,107 +1,5 @@
mitre_id,technique,tactics,groups
-T1484.002,Domain Trust Modification,Defense Evasion|Privilege Escalation,UNC2452
-T1484.001,Group Policy Modification,Defense Evasion|Privilege Escalation,no
-T1606.002,SAML Tokens,Credential Access,UNC2452
-T1606.001,Web Cookies,Credential Access,UNC2452
-T1606,Forge Web Credentials,Credential Access,no
-T1059.008,Network Device CLI,Execution,no
-T1602.002,Network Device Configuration Dump,Collection,no
-T1542.005,TFTP Boot,Defense Evasion|Persistence,no
-T1542.004,ROMMONkit,Defense Evasion|Persistence,no
-T1602.001,SNMP (MIB Dump),Collection,no
-T1602,Data from Configuration Repository,Collection,no
-T1601.002,Downgrade System Image,Defense Evasion,no
-T1601.001,Patch System Image,Defense Evasion,no
-T1601,Modify System Image,Defense Evasion,no
-T1600.002,Disable Crypto Hardware,Defense Evasion,no
-T1600.001,Reduce Key Space,Defense Evasion,no
-T1600,Weaken Encryption,Defense Evasion,no
-T1556.004,Network Device Authentication,Credential Access|Defense Evasion,no
-T1599.001,Network Address Translation Traversal,Defense Evasion,no
-T1599,Network Boundary Bridging,Defense Evasion,no
-T1020.001,Traffic Duplication,Exfiltration,no
-T1557.002,ARP Cache Poisoning,Credential Access|Collection,Cleaver
-T1588.006,Vulnerabilities,Resource Development,no
-T1053.006,Systemd Timers,Execution|Persistence|Privilege Escalation,no
-T1562.008,Disable Cloud Logs,Defense Evasion,no
-T1547.012,Print Processors,Persistence|Privilege Escalation,no
-T1598.003,Spearphishing Link,Reconnaissance,no
-T1598.002,Spearphishing Attachment,Reconnaissance,no
-T1598.001,Spearphishing Service,Reconnaissance,no
-T1598,Phishing for Information,Reconnaissance,no
-T1597.002,Purchase Technical Data,Reconnaissance,no
-T1597.001,Threat Intel Vendors,Reconnaissance,no
-T1597,Search Closed Sources,Reconnaissance,no
-T1596.005,Scan Databases,Reconnaissance,no
-T1596.004,CDNs,Reconnaissance,no
-T1596.003,Digital Certificates,Reconnaissance,no
-T1596.001,DNS/Passive DNS,Reconnaissance,no
-T1596.002,WHOIS,Reconnaissance,no
-T1596,Search Open Technical Databases,Reconnaissance,no
-T1595.002,Vulnerability Scanning,Reconnaissance,no
-T1595.001,Scanning IP Blocks,Reconnaissance,no
-T1595,Active Scanning,Reconnaissance,no
-T1594,Search Victim-Owned Websites,Reconnaissance,no
-T1593.002,Search Engines,Reconnaissance,no
-T1593.001,Social Media,Reconnaissance,no
-T1593,Search Open Websites/Domains,Reconnaissance,no
-T1592.004,Client Configurations,Reconnaissance,no
-T1592.003,Firmware,Reconnaissance,no
-T1592.002,Software,Reconnaissance,no
-T1592.001,Hardware,Reconnaissance,no
-T1592,Gather Victim Host Information,Reconnaissance,no
-T1591.004,Identify Roles,Reconnaissance,no
-T1591.003,Identify Business Tempo,Reconnaissance,no
-T1591.001,Determine Physical Locations,Reconnaissance,no
-T1591.002,Business Relationships,Reconnaissance,no
-T1591,Gather Victim Org Information,Reconnaissance,no
-T1590.006,Network Security Appliances,Reconnaissance,no
-T1590.005,IP Addresses,Reconnaissance,no
-T1590.004,Network Topology,Reconnaissance,no
-T1590.003,Network Trust Dependencies,Reconnaissance,no
-T1590.002,DNS,Reconnaissance,no
-T1590.001,Domain Properties,Reconnaissance,no
-T1590,Gather Victim Network Information,Reconnaissance,no
-T1589.003,Employee Names,Reconnaissance,no
-T1589.002,Email Addresses,Reconnaissance,no
-T1589.001,Credentials,Reconnaissance,no
-T1589,Gather Victim Identity Information,Reconnaissance,no
-T1588.005,Exploits,Resource Development,no
-T1588.004,Digital Certificates,Resource Development,no
-T1588.003,Code Signing Certificates,Resource Development,Wizard Spider
-T1588.002,Tool,Resource Development,no
-T1588.001,Malware,Resource Development,Turla|APT1
-T1588,Obtain Capabilities,Resource Development,no
-T1587.004,Exploits,Resource Development,no
-T1587.003,Digital Certificates,Resource Development,APT29|PROMETHIUM
-T1587.002,Code Signing Certificates,Resource Development,PROMETHIUM|Patchwork
-T1587.001,Malware,Resource Development,UNC2452|Turla|FIN7|Night Dragon|Cleaver
-T1587,Develop Capabilities,Resource Development,no
-T1586.002,Email Accounts,Resource Development,no
-T1586.001,Social Media Accounts,Resource Development,no
-T1586,Compromise Accounts,Resource Development,no
-T1585.002,Email Accounts,Resource Development,APT1
-T1585.001,Social Media Accounts,Resource Development,Cleaver
-T1585,Establish Accounts,Resource Development,APT17
-T1584.006,Web Services,Resource Development,Turla
-T1584.005,Botnet,Resource Development,no
-T1584.004,Server,Resource Development,Turla|APT16
-T1584.003,Virtual Private Server,Resource Development,Turla
-T1584.002,DNS Server,Resource Development,no
-T1584.001,Domains,Resource Development,APT1
-T1583.006,Web Services,Resource Development,APT17|APT29
-T1583.005,Botnet,Resource Development,no
-T1583.004,Server,Resource Development,no
-T1583.003,Virtual Private Server,Resource Development,TEMP.Veles
-T1583.002,DNS Server,Resource Development,no
-T1584,Compromise Infrastructure,Resource Development,no
-T1583.001,Domains,Resource Development,APT1|APT28
-T1583,Acquire Infrastructure,Resource Development,no
-T1564.007,VBA Stomping,Defense Evasion,no
-T1558.004,AS-REP Roasting,Credential Access,no
-T1580,Cloud Infrastructure Discovery,Discovery,no
-T1218.012,Verclsid,Defense Evasion,no
-T1205.001,Port Knocking,Defense Evasion|Persistence|Command And Control,PROMETHIUM
+T1205.001,Port Knocking,Defense Evasion|Persistence|Command And Control,no
T1564.006,Run Virtual Instance,Defense Evasion,no
T1564.005,Hidden File System,Defense Evasion,Strider|Equation
T1556.003,Pluggable Authentication Modules,Credential Access|Defense Evasion,no
@@ -109,7 +7,7 @@ T1574.012,COR_PROFILER,Persistence|Privilege Escalation|Defense Evasion,Blue Moc
T1562.007,Disable or Modify Cloud Firewall,Defense Evasion,no
T1098.004,SSH Authorized Keys,Persistence,no
T1480.001,Environmental Keying,Defense Evasion,APT41|Equation
-T1059.007,JavaScript/JScript,Execution,FIN6|APT32|FIN7|Cobalt Group|Molerats|TA505|Silence|Leafminer
+T1059.007,JavaScript/JScript,Execution,APT32|FIN7|Cobalt Group|Molerats|TA505|Silence|Leafminer
T1578.004,Revert Cloud Instance,Defense Evasion,no
T1578.003,Delete Cloud Instance,Defense Evasion,no
T1578.001,Create Snapshot,Defense Evasion,no
@@ -126,31 +24,31 @@ T1546.015,Component Object Model Hijacking,Privilege Escalation|Persistence,APT2
T1071.004,DNS,Command And Control,APT39|Tropic Trooper|OilRig|Ke3chang|Cobalt Group|APT18|APT41|FIN7
T1071.003,Mail Protocols,Command And Control,APT32|SilverTerrier|APT28
T1071.002,File Transfer Protocols,Command And Control,APT41|SilverTerrier|Machete|Honeybee
-T1071.001,Web Protocols,Command And Control,UNC2452|Sandworm Team|TA505|Rocke|APT39|Tropic Trooper|MuddyWater|Wizard Spider|Inception|APT41|SilverTerrier|Machete|APT28|WIRTE|APT33|FIN4|Night Dragon|APT18|APT38|Threat Group-3390|Ke3chang|Dark Caracal|APT19|Cobalt Group|Rancor|Orangeworm|APT37|Turla|Lazarus Group|APT32|Magic Hound|BRONZE BUTLER|OilRig|Gamaredon Group|Stealth Falcon
+T1071.001,Web Protocols,Command And Control,Sandworm Team|TA505|Rocke|APT39|Tropic Trooper|MuddyWater|Wizard Spider|Inception|APT41|SilverTerrier|Machete|APT28|WIRTE|APT33|FIN4|Night Dragon|APT18|APT38|Cobalt Group|APT19|Threat Group-3390|Rancor|Orangeworm|APT37|Ke3chang|Dark Caracal|Turla|Lazarus Group|BRONZE BUTLER|APT32|OilRig|Magic Hound|Gamaredon Group|Stealth Falcon
T1572,Protocol Tunneling,Command And Control,OilRig|Cobalt Group|FIN6
-T1048.003,Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol,Exfiltration,Wizard Spider|FIN6|APT32|APT33|Thrip|FIN8|OilRig|Lazarus Group
-T1048.002,Exfiltration Over Asymmetric Encrypted Non-C2 Protocol,Exfiltration,UNC2452
+T1048.003,Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol,Exfiltration,APT32|APT33|Thrip|FIN8|OilRig|Lazarus Group
+T1048.002,Exfiltration Over Asymmetric Encrypted Non-C2 Protocol,Exfiltration,no
T1048.001,Exfiltration Over Symmetric Encrypted Non-C2 Protocol,Exfiltration,no
T1001.003,Protocol Impersonation,Command And Control,Lazarus Group
-T1001.002,Steganography,Command And Control,APT29|Axiom
+T1001.002,Steganography,Command And Control,Axiom
T1001.001,Junk Data,Command And Control,APT28
T1132.002,Non-Standard Encoding,Command And Control,no
T1132.001,Standard Encoding,Command And Control,Sandworm Team|Tropic Trooper|MuddyWater|APT33|APT19|Lazarus Group|BRONZE BUTLER|Patchwork
T1090.004,Domain Fronting,Command And Control,APT29
T1090.003,Multi-hop Proxy,Command And Control,Inception|FIN4|APT29
T1090.002,External Proxy,Command And Control,APT39|Silence|Soft Cell|MuddyWater|APT3|FIN5|Lazarus Group|menuPass|APT28
-T1090.001,Internal Proxy,Command And Control,UNC2452|APT39|Strider
+T1090.001,Internal Proxy,Command And Control,APT39|Strider
T1102.003,One-Way Communication,Command And Control,Leviathan
-T1102.002,Bidirectional Communication,Command And Control,APT29|Sandworm Team|APT39|APT12|Turla|FIN7|APT37|Magic Hound|Carbanak
+T1102.002,Bidirectional Communication,Command And Control,Sandworm Team|APT39|APT12|Turla|FIN7|APT37|Magic Hound|Carbanak
T1102.001,Dead Drop Resolver,Command And Control,Rocke|APT41|BRONZE BUTLER|RTM|Patchwork
T1571,Non-Standard Port,Command And Control,Sandworm Team|Rocke|DarkVishnya|Silence|APT-C-36|Magic Hound|APT33|APT32|TEMP.Veles|Lazarus Group|FIN7
-T1074.002,Remote Data Staging,Collection,UNC2452|Threat Group-3390|menuPass|FIN6|Night Dragon|FIN8
-T1074.001,Local Data Staging,Collection,Machete|Soft Cell|TEMP.Veles|Honeybee|Dragonfly 2.0|Patchwork|Leviathan|APT3|FIN5|menuPass|Lazarus Group|Threat Group-3390|APT28
+T1074.002,Remote Data Staging,Collection,Threat Group-3390|menuPass|FIN6|Night Dragon|FIN8
+T1074.001,Local Data Staging,Collection,Machete|Soft Cell|TEMP.Veles|Patchwork|Dragonfly 2.0|Honeybee|Leviathan|APT3|FIN5|menuPass|FIN6|Lazarus Group|Threat Group-3390|APT28
T1078.004,Cloud Accounts,Defense Evasion|Persistence|Privilege Escalation|Initial Access,APT33
T1564.004,NTFS File Attributes,Defense Evasion,APT32
T1564.003,Hidden Window,Defense Evasion,Gorgon Group|Deep Panda|DarkHydrus|CopyKittens|APT19|APT32|APT28|APT3|Magic Hound
-T1078.003,Local Accounts,Defense Evasion|Persistence|Privilege Escalation|Initial Access,PROMETHIUM|Tropic Trooper|FIN10|Stolen Pencil|APT32
-T1078.002,Domain Accounts,Defense Evasion|Persistence|Privilege Escalation|Initial Access,Wizard Spider|APT29|TA505|APT3|Threat Group-1314
+T1078.003,Local Accounts,Defense Evasion|Persistence|Privilege Escalation|Initial Access,Tropic Trooper|FIN10|Stolen Pencil|APT32
+T1078.002,Domain Accounts,Defense Evasion|Persistence|Privilege Escalation|Initial Access,TA505|APT3|Threat Group-1314
T1078.001,Default Accounts,Defense Evasion|Persistence|Privilege Escalation|Initial Access,no
T1564.002,Hidden Users,Defense Evasion,no
T1574.006,LD_PRELOAD,Persistence|Privilege Escalation|Defense Evasion,Rocke
@@ -166,37 +64,37 @@ T1574,Hijack Execution Flow,Persistence|Privilege Escalation|Defense Evasion,no
T1069.001,Local Groups,Discovery,Turla|OilRig|admin@338
T1570,Lateral Tool Transfer,Lateral Movement,APT32|Wizard Spider|Turla|FIN10
T1568.003,DNS Calculation,Command And Control,APT12
-T1204.002,Malicious File,Execution,FIN6|PROMETHIUM|APT30|Magic Hound|Windshift|APT33|Sandworm Team|Naikon|Whitefly|Tropic Trooper|Gamaredon Group|Sharpshooter|Molerats|Wizard Spider|Mofang|Frankenstein|RTM|Inception|BlackTech|APT-C-36|Machete|admin@338|APT12|TA505|Silence|The White Company|APT39|FIN4|Darkhotel|Gallmaker|Dragonfly 2.0|Dark Caracal|FIN7|APT32|Cobalt Group|DarkHydrus|Patchwork|Rancor|MuddyWater|BRONZE BUTLER|APT19|Gorgon Group|OilRig|Lazarus Group|APT29|menuPass|TA459|FIN8|Elderwood|PLATINUM|Leviathan|APT37|APT28
-T1204.001,Malicious Link,Execution,Wizard Spider|Patchwork|Windshift|APT32|Molerats|Mofang|BlackTech|TA505|OilRig|Machete|Leviathan|FIN8|FIN4|Elderwood|Dragonfly 2.0|Cobalt Group|APT39|Night Dragon|APT33|Turla
+T1204.002,Malicious File,Execution,Magic Hound|Windshift|APT33|Sandworm Team|Naikon|Whitefly|Tropic Trooper|Gamaredon Group|Sharpshooter|Molerats|Wizard Spider|Mofang|Frankenstein|RTM|Inception|BlackTech|APT-C-36|Machete|admin@338|APT12|TA505|Silence|The White Company|APT39|FIN4|Darkhotel|Gallmaker|APT19|Dragonfly 2.0|BRONZE BUTLER|Cobalt Group|DarkHydrus|Gorgon Group|Patchwork|OilRig|Dark Caracal|MuddyWater|Lazarus Group|FIN7|APT32|Rancor|APT37|FIN8|APT28|Elderwood|TA459|APT29|Leviathan|menuPass|PLATINUM
+T1204.001,Malicious Link,Execution,Patchwork|Windshift|APT32|Molerats|Mofang|BlackTech|TA505|OilRig|Machete|Leviathan|FIN8|FIN4|Elderwood|Dragonfly 2.0|Cobalt Group|APT39|Night Dragon|APT33|Turla
T1195.003,Compromise Hardware Supply Chain,Initial Access,no
-T1195.002,Compromise Software Supply Chain,Initial Access,UNC2452|GOLD SOUTHFIELD|Dragonfly|Sandworm Team|APT41
+T1195.002,Compromise Software Supply Chain,Initial Access,Sandworm Team|APT41
T1195.001,Compromise Software Dependencies and Development Tools,Initial Access,no
-T1568.001,Fast Flux DNS,Command And Control,Machete|TA505
+T1568.001,Fast Flux DNS,Command And Control,TA505
T1052.001,Exfiltration over USB,Exfiltration,Tropic Trooper
-T1569.002,Service Execution,Execution,Wizard Spider|Blue Mockingbird|APT39|APT41|Silence|FIN6|APT32|Ke3chang|Honeybee
+T1569.002,Service Execution,Execution,Blue Mockingbird|APT39|APT41|Silence|FIN6|APT32|Honeybee|Ke3chang
T1569.001,Launchctl,Execution,no
T1569,System Services,Execution,no
T1568.002,Domain Generation Algorithms,Command And Control,APT41
-T1568,Dynamic Resolution,Command And Control,UNC2452
+T1568,Dynamic Resolution,Command And Control,no
T1011.001,Exfiltration Over Bluetooth,Exfiltration,no
T1567.002,Exfiltration to Cloud Storage,Exfiltration,Leviathan|Turla
T1567.001,Exfiltration to Code Repository,Exfiltration,no
-T1059.006,Python,Execution,APT29|Rocke|BRONZE BUTLER|APT39|Dragonfly 2.0|Machete
-T1059.005,Visual Basic,Execution,Lazarus Group|APT33|Sandworm Team|Gamaredon Group|Sharpshooter|Molerats|Frankenstein|Inception|APT-C-36|Rancor|Patchwork|MuddyWater|Honeybee|FIN7|APT37|BRONZE BUTLER|APT32|Turla|TA505|Silence|WIRTE|FIN4|Gorgon Group|Cobalt Group|Leviathan|TA459|Magic Hound
+T1059.006,Python,Execution,Rocke|BRONZE BUTLER|APT39|Dragonfly 2.0|Machete
+T1059.005,Visual Basic,Execution,APT33|Sandworm Team|Gamaredon Group|Sharpshooter|Molerats|Frankenstein|Inception|APT-C-36|Rancor|Patchwork|MuddyWater|Honeybee|FIN7|APT37|BRONZE BUTLER|APT32|Turla|TA505|Silence|WIRTE|FIN4|Cobalt Group|Gorgon Group|Leviathan|TA459|Magic Hound
T1059.004,Unix Shell,Execution,Rocke|APT41
-T1059.003,Windows Command Shell,Execution,UNC2452|Wizard Spider|FIN6|TA505|Blue Mockingbird|Tropic Trooper|Frankenstein|OilRig|Lazarus Group|Honeybee|Cobalt Group|FIN7|APT41|Soft Cell|Turla|Silence|APT32|Darkhotel|MuddyWater|APT18|APT38|Gorgon Group|Ke3chang|Dragonfly 2.0|Rancor|Dark Caracal|APT37|APT28|Leviathan|FIN8|Sowbug|Magic Hound|BRONZE BUTLER|menuPass|Threat Group-3390|FIN10|Gamaredon Group|Patchwork|Suckfly|Threat Group-1314|APT3|admin@338|APT1
+T1059.003,Windows Command Shell,Execution,TA505|Blue Mockingbird|Tropic Trooper|Frankenstein|OilRig|Lazarus Group|Honeybee|Cobalt Group|FIN7|APT41|Soft Cell|Turla|Silence|APT32|APT39|Darkhotel|MuddyWater|APT18|APT38|Dark Caracal|Gorgon Group|Dragonfly 2.0|Rancor|Ke3chang|APT37|Leviathan|FIN8|APT28|Magic Hound|Sowbug|BRONZE BUTLER|FIN10|Threat Group-3390|menuPass|Gamaredon Group|Suckfly|Patchwork|Threat Group-1314|APT3|admin@338|APT1
T1059.002,AppleScript,Execution,no
-T1059.001,PowerShell,Execution,UNC2452|Lazarus Group|Chimera|Blue Mockingbird|APT39|DarkVishnya|Molerats|Wizard Spider|Frankenstein|Inception|Silence|APT41|Kimsuky|Soft Cell|TA505|WIRTE|TEMP.Veles|APT33|Gallmaker|Turla|APT19|DarkHydrus|APT28|Gorgon Group|Thrip|Cobalt Group|Dragonfly 2.0|Leviathan|TA459|MuddyWater|FIN8|Magic Hound|CopyKittens|BRONZE BUTLER|OilRig|FIN10|Threat Group-3390|APT32|FIN7|menuPass|Patchwork|Stealth Falcon|FIN6|Poseidon Group|APT3|APT29|Deep Panda
+T1059.001,PowerShell,Execution,Blue Mockingbird|APT39|DarkVishnya|Molerats|Wizard Spider|Frankenstein|Inception|Silence|APT41|Kimsuky|Soft Cell|TA505|WIRTE|TEMP.Veles|APT33|Gallmaker|Turla|APT19|DarkHydrus|APT28|Thrip|Gorgon Group|Cobalt Group|Dragonfly 2.0|Leviathan|TA459|FIN8|MuddyWater|Magic Hound|OilRig|BRONZE BUTLER|CopyKittens|APT32|FIN7|FIN10|Threat Group-3390|menuPass|Patchwork|Stealth Falcon|FIN6|Poseidon Group|APT3|APT29|Deep Panda
T1567,Exfiltration Over Web Service,Exfiltration,no
T1497.003,Time Based Evasion,Defense Evasion|Discovery,no
T1497.002,User Activity Based Checks,Defense Evasion|Discovery,FIN7
T1497.001,System Checks,Defense Evasion|Discovery,Frankenstein
T1498.002,Reflection Amplification,Impact,no
T1498.001,Direct Network Flood,Impact,no
-T1566.003,Spearphishing via Service,Initial Access,Lazarus Group|Magic Hound|Windshift|FIN6|OilRig|Dark Caracal
-T1566.002,Spearphishing Link,Initial Access,Wizard Spider|APT1|Windshift|Molerats|Mofang|BlackTech|Machete|Kimsuky|TA505|Stolen Pencil|APT39|FIN4|APT32|Night Dragon|APT28|Cobalt Group|Dragonfly 2.0|Turla|OilRig|APT33|Leviathan|Patchwork|Elderwood|APT29|Magic Hound|FIN8
-T1566.001,Spearphishing Attachment,Initial Access,APT1|FIN6|APT30|Magic Hound|Windshift|APT33|Sandworm Team|Naikon|Gamaredon Group|Sharpshooter|Molerats|Mofang|Wizard Spider|RTM|Frankenstein|Inception|BlackTech|APT-C-36|APT41|Machete|admin@338|Kimsuky|APT12|TA505|Silence|The White Company|APT39|FIN4|Darkhotel|Gallmaker|Tropic Trooper|Turla|Lazarus Group|Cobalt Group|FIN7|OilRig|BRONZE BUTLER|APT32|Gorgon Group|Rancor|DarkHydrus|APT19|Dragonfly 2.0|FIN8|PLATINUM|MuddyWater|TA459|Leviathan|Elderwood|APT29|APT37|menuPass|APT28|Patchwork
-T1566,Phishing,Initial Access,GOLD SOUTHFIELD|Dragonfly
+T1566.003,Spearphishing via Service,Initial Access,Magic Hound|Windshift|FIN6|OilRig|Dark Caracal
+T1566.002,Spearphishing Link,Initial Access,Windshift|Molerats|Mofang|BlackTech|Machete|Kimsuky|TA505|Stolen Pencil|APT39|FIN4|APT32|Night Dragon|Turla|APT28|Cobalt Group|Dragonfly 2.0|OilRig|APT33|Elderwood|Leviathan|Magic Hound|Patchwork|APT29|FIN8
+T1566.001,Spearphishing Attachment,Initial Access,Magic Hound|Windshift|APT33|Sandworm Team|Naikon|Gamaredon Group|Sharpshooter|Molerats|Mofang|Wizard Spider|RTM|Frankenstein|Inception|BlackTech|APT-C-36|APT41|Machete|admin@338|Kimsuky|APT12|TA505|Silence|The White Company|APT39|FIN4|Darkhotel|Gallmaker|Tropic Trooper|Turla|Gorgon Group|Rancor|DarkHydrus|Cobalt Group|FIN7|OilRig|Lazarus Group|APT19|Dragonfly 2.0|BRONZE BUTLER|APT32|FIN8|MuddyWater|APT28|TA459|Leviathan|Patchwork|PLATINUM|Elderwood|APT29|APT37|menuPass
+T1566,Phishing,Initial Access,no
T1565.003,Runtime Data Manipulation,Impact,APT38
T1565.002,Transmitted Data Manipulation,Impact,APT38
T1565.001,Stored Data Manipulation,Impact,FIN4|APT38
@@ -206,18 +104,18 @@ T1564,Hide Artifacts,Defense Evasion,no
T1563.002,RDP Hijacking,Lateral Movement,no
T1563.001,SSH Hijacking,Lateral Movement,no
T1563,Remote Service Session Hijacking,Lateral Movement,no
-T1518.001,Security Software Discovery,Discovery,Wizard Spider|Turla|Rocke|Frankenstein|The White Company|Cobalt Group|Darkhotel|MuddyWater|Tropic Trooper|FIN8|Patchwork|Naikon
+T1518.001,Security Software Discovery,Discovery,Turla|Rocke|Frankenstein|The White Company|Cobalt Group|Darkhotel|MuddyWater|Tropic Trooper|FIN8|Patchwork|Naikon
T1069.003,Cloud Groups,Discovery,no
T1069.002,Domain Groups,Discovery,Turla|Wizard Spider|Inception|OilRig|FIN6|Dragonfly 2.0|Ke3chang
T1087.004,Cloud Account,Discovery,no
T1087.003,Email Account,Discovery,Sandworm Team|TA505
-T1087.002,Domain Account,Discovery,Wizard Spider|Chimera|Turla|Sandworm Team|Dragonfly 2.0|OilRig|BRONZE BUTLER|menuPass|FIN6|Poseidon Group|Ke3chang
+T1087.002,Domain Account,Discovery,Turla|Sandworm Team|Dragonfly 2.0|OilRig|BRONZE BUTLER|menuPass|FIN6|Poseidon Group|Ke3chang
T1087.001,Local Account,Discovery,Turla|Poseidon Group|OilRig|Ke3chang|APT32|APT1|Threat Group-3390|APT3|admin@338
T1553.004,Install Root Certificate,Defense Evasion,no
-T1562.004,Disable or Modify System Firewall,Defense Evasion,UNC2452|Rocke|Lazarus Group|Kimsuky|Dragonfly 2.0|Carbanak
-T1562.003,Impair Command History Logging,Defense Evasion,no
-T1562.002,Disable Windows Event Logging,Defense Evasion,UNC2452|Threat Group-3390
-T1562.001,Disable or Modify Tools,Defense Evasion,UNC2452|Wizard Spider|FIN6|Gamaredon Group|BRONZE BUTLER|Rocke|Kimsuky|Turla|Night Dragon|Gorgon Group|Lazarus Group|Putter Panda
+T1562.004,Disable or Modify System Firewall,Defense Evasion,Rocke|Lazarus Group|Kimsuky|Dragonfly 2.0|Carbanak
+T1562.003,HISTCONTROL,Defense Evasion,no
+T1562.002,Disable Windows Event Logging,Defense Evasion,Threat Group-3390
+T1562.001,Disable or Modify Tools,Defense Evasion,Gamaredon Group|BRONZE BUTLER|Rocke|Kimsuky|Turla|Night Dragon|Gorgon Group|Lazarus Group|Putter Panda
T1562,Impair Defenses,Defense Evasion,no
T1003.004,LSA Secrets,Credential Access,OilRig|MuddyWater|menuPass|Leafminer|Ke3chang|Dragonfly 2.0|APT33|Threat Group-3390
T1003.005,Cached Domain Credentials,Credential Access,OilRig|MuddyWater|Leafminer|APT33
@@ -226,8 +124,8 @@ T1561.001,Disk Content Wipe,Impact,Lazarus Group
T1561,Disk Wipe,Impact,no
T1560.003,Archive via Custom Method,Collection,Lazarus Group|Kimsuky|CopyKittens|FIN6
T1560.002,Archive via Library,Collection,Lazarus Group|Threat Group-3390
-T1560.001,Archive via Utility,Collection,UNC2452|Chimera|APT41|Soft Cell|Turla|Gallmaker|APT33|APT39|MuddyWater|Magic Hound|FIN8|BRONZE BUTLER|CopyKittens|Sowbug|APT3|menuPass|APT1|Ke3chang
-T1560,Archive Collected Data,Collection,menuPass|APT32|Patchwork|APT28|Dragonfly 2.0|Honeybee|FIN6|Lazarus Group|Ke3chang
+T1560.001,Archive via Utility,Collection,APT41|Soft Cell|Turla|Gallmaker|APT33|APT39|MuddyWater|Magic Hound|FIN8|BRONZE BUTLER|CopyKittens|APT3|Sowbug|menuPass|APT1|Ke3chang
+T1560,Archive Collected Data,Collection,menuPass|APT32|Honeybee|Patchwork|APT28|Dragonfly 2.0|FIN6|Lazarus Group|Ke3chang
T1499.004,Application or System Exploitation,Impact,no
T1499.003,Application Exhaustion Flood,Impact,no
T1499.002,Service Exhaustion Flood,Impact,no
@@ -235,7 +133,7 @@ T1499.001,OS Exhaustion Flood,Impact,no
T1491.002,External Defacement,Impact,no
T1491.001,Internal Defacement,Impact,Lazarus Group
T1114.003,Email Forwarding Rule,Collection,no
-T1114.002,Remote Email Collection,Collection,UNC2452|APT1|FIN4|Ke3chang|Leafminer|Dragonfly 2.0|APT28
+T1114.002,Remote Email Collection,Collection,APT1|FIN4|APT28|Dragonfly 2.0|Ke3chang|Leafminer
T1114.001,Local Email Collection,Collection,Magic Hound|APT1
T1134.005,SID-History Injection,Defense Evasion|Privilege Escalation,no
T1134.004,Parent PID Spoofing,Defense Evasion|Privilege Escalation,no
@@ -244,93 +142,93 @@ T1134.002,Create Process with Token,Defense Evasion|Privilege Escalation,Turla|L
T1134.001,Token Impersonation/Theft,Defense Evasion|Privilege Escalation,APT28
T1213.002,Sharepoint,Collection,Ke3chang|APT28
T1213.001,Confluence,Collection,no
-T1555.003,Credentials from Web Browsers,Credential Access,FIN6|Magic Hound|Sandworm Team|Inception|Stealth Falcon|OilRig|Leafminer|APT33|APT3|Kimsuky|TA505|Stolen Pencil|MuddyWater|APT37|Patchwork|Molerats
+T1555.003,Credentials from Web Browsers,Credential Access,Magic Hound|Sandworm Team|Inception|Stealth Falcon|OilRig|Leafminer|APT33|APT3|Kimsuky|TA505|Stolen Pencil|MuddyWater|APT37|Patchwork|Molerats
T1555.002,Securityd Memory,Credential Access,no
T1555.001,Keychain,Credential Access,no
-T1559.002,Dynamic Data Exchange,Execution,Sharpshooter|TA505|MuddyWater|Gallmaker|Cobalt Group|Patchwork|APT37|FIN7|APT28
+T1559.002,Dynamic Data Exchange,Execution,Sharpshooter|TA505|MuddyWater|Gallmaker|Patchwork|Cobalt Group|APT37|APT28|FIN7
T1559.001,Component Object Model,Execution,Gamaredon Group|MuddyWater
T1559,Inter-Process Communication,Execution,no
T1558.002,Silver Ticket,Credential Access,no
T1558.001,Golden Ticket,Credential Access,Ke3chang
T1558,Steal or Forge Kerberos Tickets,Credential Access,no
-T1557.001,LLMNR/NBT-NS Poisoning and SMB Relay,Credential Access|Collection,Wizard Spider
+T1557.001,LLMNR/NBT-NS Poisoning and SMB Relay,Credential Access|Collection,no
T1557,Man-in-the-Middle,Credential Access|Collection,no
T1556.002,Password Filter DLL,Credential Access|Defense Evasion,Strider
-T1556.001,Domain Controller Authentication,Credential Access|Defense Evasion,Chimera
+T1556.001,Domain Controller Authentication,Credential Access|Defense Evasion,no
T1556,Modify Authentication Process,Credential Access|Defense Evasion,no
T1056.004,Credential API Hooking,Collection|Credential Access,PLATINUM
T1056.003,Web Portal Capture,Collection|Credential Access,no
T1056.002,GUI Input Capture,Collection|Credential Access,FIN4
-T1056.001,Keylogging,Collection|Credential Access,APT32|Sandworm Team|APT39|APT41|Kimsuky|menuPass|Stolen Pencil|FIN4|APT38|OilRig|Ke3chang|PLATINUM|Sowbug|Magic Hound|Group5|Lazarus Group|Threat Group-3390|APT3|Darkhotel|APT28
-T1555,Credentials from Password Stores,Credential Access,UNC2452|FIN6|APT39|OilRig|MuddyWater|Leafminer|APT33|Turla|Stealth Falcon
+T1056.001,Keylogging,Collection|Credential Access,APT32|Sandworm Team|APT39|APT41|Kimsuky|menuPass|Stolen Pencil|FIN4|APT38|Ke3chang|OilRig|PLATINUM|Sowbug|Magic Hound|Group5|Lazarus Group|Threat Group-3390|APT3|Darkhotel|APT28
+T1555,Credentials from Password Stores,Credential Access,APT39|OilRig|MuddyWater|Leafminer|APT33|Turla|Stealth Falcon
T1552.005,Cloud Instance Metadata API,Credential Access,no
T1003.008,/etc/passwd and /etc/shadow,Credential Access,no
T1003.007,Proc Filesystem,Credential Access,no
-T1003.006,DCSync,Credential Access,UNC2452
-T1558.003,Kerberoasting,Credential Access,UNC2452|Wizard Spider
+T1003.006,DCSync,Credential Access,no
+T1558.003,Kerberoasting,Credential Access,no
T1552.006,Group Policy Preferences,Credential Access,APT33
-T1003.003,NTDS,Credential Access,Wizard Spider|Chimera|FIN6|Dragonfly 2.0
-T1003.002,Security Account Manager,Credential Access,Wizard Spider|Threat Group-3390|Ke3chang|Soft Cell|Night Dragon|Dragonfly 2.0|menuPass
-T1003.001,LSASS Memory,Credential Access,Sandworm Team|Whitefly|Blue Mockingbird|Silence|Threat Group-3390|Leviathan|APT41|Soft Cell|TEMP.Veles|APT33|APT39|Stolen Pencil|APT32|Leafminer|Lazarus Group|Magic Hound|MuddyWater|FIN8|PLATINUM|OilRig|BRONZE BUTLER|FIN6|APT3|APT28|APT1|Ke3chang|Cleaver
+T1003.003,NTDS,Credential Access,FIN6|Dragonfly 2.0
+T1003.002,Security Account Manager,Credential Access,Threat Group-3390|Ke3chang|Soft Cell|Night Dragon|Dragonfly 2.0|menuPass
+T1003.001,LSASS Memory,Credential Access,Sandworm Team|Whitefly|Blue Mockingbird|Silence|Threat Group-3390|Leviathan|APT41|Soft Cell|TEMP.Veles|APT33|APT39|Stolen Pencil|APT32|Lazarus Group|Leafminer|Magic Hound|MuddyWater|PLATINUM|FIN8|BRONZE BUTLER|OilRig|FIN6|APT3|APT28|APT1|Ke3chang|Cleaver
T1110.004,Credential Stuffing,Credential Access,no
-T1110.003,Password Spraying,Credential Access,APT28|APT33|Leafminer|Lazarus Group
-T1110.002,Password Cracking,Credential Access,FIN6|APT41|Dragonfly 2.0|APT3
-T1110.001,Password Guessing,Credential Access,APT28
-T1021.006,Windows Remote Management,Lateral Movement,UNC2452|Wizard Spider|Threat Group-3390
+T1110.003,Password Spraying,Credential Access,APT33|Leafminer|Lazarus Group
+T1110.002,Password Cracking,Credential Access,APT41|Dragonfly 2.0|APT3
+T1110.001,Password Guessing,Credential Access,no
+T1021.006,Windows Remote Management,Lateral Movement,Threat Group-3390
T1021.005,VNC,Lateral Movement,GCMAN
T1021.004,SSH,Lateral Movement,Rocke|TEMP.Veles|Leviathan|APT39|OilRig|menuPass|GCMAN
T1021.003,Distributed Component Object Model,Lateral Movement,no
-T1021.002,SMB/Windows Admin Shares,Lateral Movement,Wizard Spider|Chimera|Blue Mockingbird|APT39|APT32|Orangeworm|FIN8|APT3|Lazarus Group|Threat Group-1314|Turla|Deep Panda|Ke3chang
-T1021.001,Remote Desktop Protocol,Lateral Movement,Chimera|Blue Mockingbird|Wizard Spider|Silence|APT41|TEMP.Veles|Leviathan|APT39|Stolen Pencil|Cobalt Group|Dragonfly 2.0|FIN8|APT3|OilRig|FIN10|menuPass|Patchwork|FIN6|Lazarus Group|APT1|Axiom
+T1021.002,SMB/Windows Admin Shares,Lateral Movement,Blue Mockingbird|APT39|APT32|Orangeworm|FIN8|APT3|Lazarus Group|Threat Group-1314|Turla|Deep Panda|Ke3chang
+T1021.001,Remote Desktop Protocol,Lateral Movement,Blue Mockingbird|Wizard Spider|Silence|APT41|TEMP.Veles|Leviathan|APT39|Stolen Pencil|Cobalt Group|Dragonfly 2.0|FIN8|APT3|OilRig|menuPass|FIN10|Patchwork|FIN6|Lazarus Group|APT1|Axiom
T1554,Compromise Client Software Binary,Persistence,no
T1036.006,Space after Filename,Defense Evasion,no
-T1036.005,Match Legitimate Name or Location,Defense Evasion,UNC2452|Chimera|PROMETHIUM|Rocke|Sandworm Team|APT39|Blue Mockingbird|Whitefly|Tropic Trooper|Silence|APT41|menuPass|TEMP.Veles|MuddyWater|BRONZE BUTLER|Sowbug|APT32|Patchwork|Poseidon Group|admin@338|Carbanak|APT1
-T1036.004,Masquerade Task or Service,Defense Evasion,UNC2452|Lazarus Group|PROMETHIUM|Wizard Spider|APT-C-36|Carbanak|APT32|FIN6|FIN7
+T1036.005,Match Legitimate Name or Location,Defense Evasion,Rocke|Sandworm Team|APT39|Blue Mockingbird|Whitefly|Tropic Trooper|Silence|APT41|menuPass|TEMP.Veles|MuddyWater|BRONZE BUTLER|Sowbug|APT32|Patchwork|Poseidon Group|admin@338|Carbanak|APT1
+T1036.004,Masquerade Task or Service,Defense Evasion,Wizard Spider|APT-C-36|Carbanak|APT32|FIN6|FIN7
T1036.003,Rename System Utilities,Defense Evasion,menuPass|APT32|Soft Cell|PLATINUM
T1036.002,Right-to-Left Override,Defense Evasion,BRONZE BUTLER|BlackTech|Ke3chang|Scarlet Mimic
-T1036.001,Invalid Code Signature,Defense Evasion,Windshift|APT37
+T1036.001,Invalid Code Signature,Defense Evasion,Windshift
T1553.003,SIP and Trust Provider Hijacking,Defense Evasion,no
-T1553.002,Code Signing,Defense Evasion,UNC2452|Wizard Spider|PROMETHIUM|Patchwork|Silence|APT41|FIN6|TA505|FIN7|Honeybee|Leviathan|CopyKittens|Winnti Group|Suckfly|Molerats|Darkhotel
+T1553.002,Code Signing,Defense Evasion,Patchwork|Silence|APT41|FIN6|TA505|FIN7|Honeybee|Leviathan|APT37|CopyKittens|Winnti Group|Suckfly|Molerats|Darkhotel
T1553.001,Gatekeeper Bypass,Defense Evasion,no
T1553,Subvert Trust Controls,Defense Evasion,no
T1027.003,Steganography,Defense Evasion,BRONZE BUTLER|Tropic Trooper|MuddyWater|APT37
-T1027.002,Software Packing,Defense Evasion,Lazarus Group|TA505|Rocke|Soft Cell|The White Company|APT39|APT38|Dark Caracal|Elderwood|APT3|Patchwork|APT29|Night Dragon
-T1027.001,Binary Padding,Defense Evasion,Gamaredon Group|APT32|Patchwork|Leviathan|BRONZE BUTLER|Moafee
+T1027.002,Software Packing,Defense Evasion,TA505|Rocke|Soft Cell|The White Company|APT39|APT38|Dark Caracal|Elderwood|APT3|Patchwork|APT29|Night Dragon
+T1027.001,Binary Padding,Defense Evasion,Gamaredon Group|Patchwork|APT32|Leviathan|BRONZE BUTLER|Moafee
T1222.002,Linux and Mac File and Directory Permissions Modification,Defense Evasion,Rocke|APT32
-T1222.001,Windows File and Directory Permissions Modification,Defense Evasion,Wizard Spider
-T1552.004,Private Keys,Credential Access,UNC2452|Rocke
+T1222.001,Windows File and Directory Permissions Modification,Defense Evasion,no
+T1552.004,Private Keys,Credential Access,Rocke
T1552.003,Bash History,Credential Access,no
T1552.002,Credentials in Registry,Credential Access,APT32
T1552.001,Credentials In Files,Credential Access,Leafminer|APT33|OilRig|TA505|Stolen Pencil|MuddyWater|APT3
T1552,Unsecured Credentials,Credential Access,no
T1216.001,PubPrn,Defense Evasion,APT32
-T1070.006,Timestomp,Defense Evasion,UNC2452|Rocke|TEMP.Veles|APT32|Lazarus Group|APT28
+T1070.006,Timestomp,Defense Evasion,Rocke|TEMP.Veles|APT32|Lazarus Group|APT28
T1070.005,Network Share Connection Removal,Defense Evasion,Threat Group-3390
-T1070.004,File Deletion,Defense Evasion,UNC2452|FIN6|Sandworm Team|Rocke|Tropic Trooper|Gamaredon Group|Wizard Spider|APT41|Kimsuky|Silence|The White Company|TEMP.Veles|APT32|APT38|Honeybee|Patchwork|Cobalt Group|Dragonfly 2.0|menuPass|FIN8|BRONZE BUTLER|FIN5|APT3|OilRig|Magic Hound|FIN10|APT28|Threat Group-3390|Group5|Lazarus Group|APT18|APT29
+T1070.004,File Deletion,Defense Evasion,Sandworm Team|Rocke|Tropic Trooper|Gamaredon Group|Wizard Spider|APT41|Kimsuky|Silence|The White Company|TEMP.Veles|APT32|APT38|Patchwork|Honeybee|Cobalt Group|Dragonfly 2.0|menuPass|FIN8|OilRig|FIN5|BRONZE BUTLER|Magic Hound|APT3|FIN10|APT28|Threat Group-3390|Group5|Lazarus Group|APT18|APT29
T1070.003,Clear Command History,Defense Evasion,APT41
-T1550.004,Web Session Cookie,Defense Evasion|Lateral Movement,UNC2452
+T1550.004,Web Session Cookie,Defense Evasion|Lateral Movement,no
T1550.001,Application Access Token,Defense Evasion|Lateral Movement,APT28
T1550.003,Pass the Ticket,Defense Evasion|Lateral Movement,APT32|BRONZE BUTLER|APT29
T1550.002,Pass the Hash,Defense Evasion|Lateral Movement,Soft Cell|APT32|Night Dragon|APT28|APT1
-T1550,Use Alternate Authentication Material,Defense Evasion|Lateral Movement,UNC2452
+T1550,Use Alternate Authentication Material,Defense Evasion|Lateral Movement,no
T1548.004,Elevated Execution with Prompt,Privilege Escalation|Defense Evasion,no
T1548.003,Sudo and Sudo Caching,Privilege Escalation|Defense Evasion,no
-T1548.002,Bypass User Account Control,Privilege Escalation|Defense Evasion,APT37|MuddyWater|Honeybee|Cobalt Group|Threat Group-3390|BRONZE BUTLER|Patchwork|APT29
+T1548.002,Bypass User Access Control,Privilege Escalation|Defense Evasion,APT37|MuddyWater|Honeybee|Cobalt Group|Threat Group-3390|BRONZE BUTLER|Patchwork|APT29
T1548.001,Setuid and Setgid,Privilege Escalation|Defense Evasion,no
T1548,Abuse Elevation Control Mechanism,Privilege Escalation|Defense Evasion,no
T1136.003,Cloud Account,Persistence,no
T1070.002,Clear Linux or Mac System Logs,Defense Evasion,Rocke
T1070.001,Clear Windows Event Logs,Defense Evasion,APT41|APT38|Dragonfly 2.0|APT32|FIN8|FIN5|APT28
T1136.002,Domain Account,Persistence,Soft Cell
-T1136.001,Local Account,Persistence,APT39|APT41|Leafminer|Dragonfly 2.0|APT3
+T1136.001,Local Account,Persistence,APT39|APT41|Dragonfly 2.0|Leafminer|APT3
T1547.011,Plist Modification,Persistence|Privilege Escalation,no
T1547.010,Port Monitors,Persistence|Privilege Escalation,no
T1547.009,Shortcut Modification,Persistence|Privilege Escalation,APT39|Darkhotel|APT29|Gorgon Group|Dragonfly 2.0|Leviathan|Lazarus Group
T1547.008,LSASS Driver,Persistence|Privilege Escalation,no
T1547.007,Re-opened Applications,Persistence|Privilege Escalation,no
T1547.006,Kernel Modules and Extensions,Persistence|Privilege Escalation,no
-T1547.005,Security Support Provider,Persistence|Privilege Escalation,Lazarus Group
-T1547.004,Winlogon Helper DLL,Persistence|Privilege Escalation,Wizard Spider|Tropic Trooper|Turla
+T1547.005,Security Support Provider,Persistence|Privilege Escalation,no
+T1547.004,Winlogon Helper DLL,Persistence|Privilege Escalation,Tropic Trooper|Turla
T1547.003,Time Providers,Persistence|Privilege Escalation,no
T1546.014,Emond,Privilege Escalation|Persistence,no
T1546.013,PowerShell Profile,Privilege Escalation|Persistence,Turla
@@ -346,30 +244,30 @@ T1546.007,Netsh Helper DLL,Privilege Escalation|Persistence,no
T1546.006,LC_LOAD_DYLIB Addition,Privilege Escalation|Persistence,no
T1546.005,Trap,Privilege Escalation|Persistence,no
T1546.004,.bash_profile and .bashrc,Privilege Escalation|Persistence,no
-T1546.003,Windows Management Instrumentation Event Subscription,Privilege Escalation|Persistence,UNC2452|APT33|Blue Mockingbird|Turla|Leviathan|APT29
+T1546.003,Windows Management Instrumentation Event Subscription,Privilege Escalation|Persistence,APT33|Blue Mockingbird|Turla|Leviathan|APT29
T1546.002,Screensaver,Privilege Escalation|Persistence,no
T1546.001,Change Default File Association,Privilege Escalation|Persistence,Kimsuky
-T1547.001,Registry Run Keys / Startup Folder,Persistence|Privilege Escalation,Wizard Spider|PROMETHIUM|Rocke|Tropic Trooper|Gamaredon Group|Sharpshooter|Molerats|Silence|RTM|Inception|APT41|Machete|Kimsuky|APT33|APT39|APT32|APT18|Turla|Dark Caracal|Cobalt Group|Honeybee|APT19|Ke3chang|Threat Group-3390|Dragonfly 2.0|Gorgon Group|MuddyWater|APT37|Leviathan|BRONZE BUTLER|Magic Hound|APT3|FIN10|FIN7|Patchwork|FIN6|Lazarus Group|Putter Panda|APT29|Darkhotel
+T1547.001,Registry Run Keys / Startup Folder,Persistence|Privilege Escalation,Rocke|Tropic Trooper|Gamaredon Group|Sharpshooter|Molerats|Silence|RTM|Inception|APT41|Machete|Kimsuky|APT33|APT39|APT32|APT18|Turla|Dark Caracal|Cobalt Group|Honeybee|Threat Group-3390|Dragonfly 2.0|Gorgon Group|Ke3chang|APT19|Leviathan|MuddyWater|APT37|BRONZE BUTLER|Magic Hound|APT3|FIN10|FIN7|Patchwork|FIN6|Lazarus Group|Putter Panda|APT29|Darkhotel
T1218.002,Control Panel,Defense Evasion,no
-T1218.010,Regsvr32,Defense Evasion,Blue Mockingbird|Inception|WIRTE|APT19|Cobalt Group|Leviathan|APT32|Deep Panda
+T1218.010,Regsvr32,Defense Evasion,Blue Mockingbird|Inception|WIRTE|Cobalt Group|APT19|Leviathan|APT32|Deep Panda
T1218.009,Regsvcs/Regasm,Defense Evasion,no
-T1218.005,Mshta,Defense Evasion,Lazarus Group|Inception|Kimsuky|APT32|MuddyWater|FIN7
-T1218.004,InstallUtil,Defense Evasion,menuPass
-T1218.001,Compiled HTML File,Defense Evasion,APT41|Silence|OilRig|Lazarus Group|Dark Caracal
+T1218.005,Mshta,Defense Evasion,Inception|Kimsuky|APT32|MuddyWater|FIN7
+T1218.004,InstallUtil,Defense Evasion,no
+T1218.001,Compiled HTML File,Defense Evasion,APT41|Silence|Lazarus Group|Dark Caracal|OilRig
T1218.003,CMSTP,Defense Evasion,Cobalt Group|MuddyWater
-T1218.011,Rundll32,Defense Evasion,UNC2452|Gamaredon Group|APT32|Sandworm Team|Blue Mockingbird|TA505|MuddyWater|APT29|APT19|CopyKittens|APT3|Carbanak|APT28
+T1218.011,Rundll32,Defense Evasion,APT32|Sandworm Team|Blue Mockingbird|TA505|MuddyWater|APT29|APT19|CopyKittens|APT3|Carbanak|APT28
T1547,Boot or Logon Autostart Execution,Persistence|Privilege Escalation,no
T1546,Event Triggered Execution,Privilege Escalation|Persistence,no
T1098.003,Add Office 365 Global Administrator Role,Persistence,no
-T1098.002,Exchange Email Delegate Permissions,Persistence,UNC2452|Magic Hound
-T1098.001,Additional Cloud Credentials,Persistence,UNC2452
+T1098.002,Exchange Email Delegate Permissions,Persistence,Magic Hound
+T1098.001,Additional Azure Service Principal Credentials,Persistence,no
T1543.004,Launch Daemon,Persistence|Privilege Escalation,no
-T1543.003,Windows Service,Persistence|Privilege Escalation,PROMETHIUM|Blue Mockingbird|DarkVishnya|Wizard Spider|APT32|APT41|Kimsuky|Tropic Trooper|Threat Group-3390|Honeybee|Cobalt Group|Ke3chang|FIN7|APT19|APT3|Lazarus Group|Carbanak
+T1543.003,Windows Service,Persistence|Privilege Escalation,Blue Mockingbird|DarkVishnya|Wizard Spider|APT32|APT41|Kimsuky|Tropic Trooper|Cobalt Group|Ke3chang|Honeybee|FIN7|Threat Group-3390|APT19|APT3|Lazarus Group|Carbanak
T1543.002,Systemd Service,Persistence|Privilege Escalation,Rocke
T1543.001,Launch Agent,Persistence|Privilege Escalation,no
T1037.005,Startup Items,Persistence|Privilege Escalation,no
T1037.004,Rc.common,Persistence|Privilege Escalation,no
-T1055.012,Process Hollowing,Defense Evasion|Privilege Escalation,menuPass|Gorgon Group|Threat Group-3390|Patchwork
+T1055.012,Process Hollowing,Defense Evasion|Privilege Escalation,Threat Group-3390|menuPass|Gorgon Group|Patchwork
T1055.013,Process Doppelgänging,Defense Evasion|Privilege Escalation,Leafminer
T1055.011,Extra Window Memory Injection,Defense Evasion|Privilege Escalation,no
T1055.014,VDSO Hijacking,Defense Evasion|Privilege Escalation,no
@@ -379,7 +277,7 @@ T1055.005,Thread Local Storage,Defense Evasion|Privilege Escalation,no
T1055.004,Asynchronous Procedure Call,Defense Evasion|Privilege Escalation,no
T1055.003,Thread Execution Hijacking,Defense Evasion|Privilege Escalation,no
T1055.002,Portable Executable Injection,Defense Evasion|Privilege Escalation,Rocke|Gorgon Group
-T1055.001,Dynamic-link Library Injection,Defense Evasion|Privilege Escalation,Wizard Spider|TA505|Turla|Tropic Trooper|Lazarus Group|Putter Panda
+T1055.001,Dynamic-link Library Injection,Defense Evasion|Privilege Escalation,TA505|Turla|Tropic Trooper|Lazarus Group|Putter Panda
T1037.003,Network Logon Script,Persistence|Privilege Escalation,no
T1543,Create or Modify System Process,Persistence|Privilege Escalation,no
T1037.002,Logon Script (Mac),Persistence|Privilege Escalation,no
@@ -393,7 +291,7 @@ T1505.001,SQL Stored Procedures,Persistence,no
T1053.003,Cron,Execution|Persistence|Privilege Escalation,Rocke
T1053.004,Launchd,Execution|Persistence|Privilege Escalation,no
T1053.001,At (Linux),Execution|Persistence|Privilege Escalation,no
-T1053.005,Scheduled Task,Execution|Persistence|Privilege Escalation,UNC2452|Chimera|Gamaredon Group|Blue Mockingbird|MuddyWater|Wizard Spider|Frankenstein|APT-C-36|BRONZE BUTLER|APT41|Machete|Soft Cell|Silence|TEMP.Veles|APT33|APT39|Cobalt Group|OilRig|Rancor|Dragonfly 2.0|Patchwork|FIN8|FIN7|APT32|menuPass|FIN10|Stealth Falcon|FIN6|APT3|APT29
+T1053.005,Scheduled Task,Execution|Persistence|Privilege Escalation,Gamaredon Group|Blue Mockingbird|MuddyWater|Wizard Spider|Frankenstein|APT-C-36|BRONZE BUTLER|APT41|Machete|Soft Cell|Silence|TEMP.Veles|APT33|APT39|Dragonfly 2.0|Patchwork|OilRig|Rancor|Cobalt Group|FIN8|menuPass|FIN10|APT32|FIN7|Stealth Falcon|FIN6|APT3|APT29
T1053.002,At (Windows),Execution|Persistence|Privilege Escalation,BRONZE BUTLER|Threat Group-3390|APT18
T1542,Pre-OS Boot,Defense Evasion|Persistence,no
T1137.001,Office Template Macros,Persistence,MuddyWater
@@ -418,52 +316,52 @@ T1526,Cloud Service Discovery,Discovery,no
T1505,Server Software Component,Persistence,no
T1499,Endpoint Denial of Service,Impact,no
T1497,Virtualization/Sandbox Evasion,Defense Evasion|Discovery,no
-T1498,Network Denial of Service,Impact,APT28
+T1498,Network Denial of Service,Impact,no
T1496,Resource Hijacking,Impact,Blue Mockingbird|Rocke|APT41|Lazarus Group
T1495,Firmware Corruption,Impact,no
T1491,Defacement,Impact,no
T1490,Inhibit System Recovery,Impact,no
-T1489,Service Stop,Impact,Wizard Spider|Lazarus Group
+T1489,Service Stop,Impact,Lazarus Group
T1486,Data Encrypted for Impact,Impact,APT41|TA505|APT38
T1485,Data Destruction,Impact,Sandworm Team|Lazarus Group|APT38
-T1484,Domain Policy Modification,Defense Evasion|Privilege Escalation,no
-T1482,Domain Trust Discovery,Discovery,UNC2452|Wizard Spider
+T1484,Group Policy Modification,Defense Evasion|Privilege Escalation,no
+T1482,Domain Trust Discovery,Discovery,Wizard Spider
T1480,Execution Guardrails,Defense Evasion,no
-T1220,XSL Script Processing,Defense Evasion,Cobalt Group
T1222,File and Directory Permissions Modification,Defense Evasion,no
T1221,Template Injection,Defense Evasion,Gamaredon Group|Frankenstein|Inception|APT28|Tropic Trooper|Dragonfly 2.0|DarkHydrus
-T1203,Exploitation for Client Execution,Execution,Sandworm Team|MuddyWater|Frankenstein|Inception|BlackTech|APT41|admin@338|Threat Group-3390|APT12|The White Company|APT33|APT32|APT28|Tropic Trooper|BRONZE BUTLER|Lazarus Group|Cobalt Group|APT29|Patchwork|Leviathan|APT37|Elderwood|TA459
-T1200,Hardware Additions,Initial Access,DarkVishnya
-T1202,Indirect Command Execution,Defense Evasion,no
-T1213,Data from Information Repositories,Collection,FIN6|Turla
-T1207,Rogue Domain Controller,Defense Evasion,no
-T1204,User Execution,Execution,no
-T1217,Browser Bookmark Discovery,Discovery,no
-T1190,Exploit Public-Facing Application,Initial Access,UNC2452|APT28|APT29|GOLD SOUTHFIELD|Blue Mockingbird|Rocke|APT39|BlackTech|APT41|Soft Cell|Night Dragon|Axiom
-T1210,Exploitation of Remote Services,Lateral Movement,Wizard Spider|Threat Group-3390|APT28
+T1220,XSL Script Processing,Defense Evasion,Cobalt Group
T1197,BITS Jobs,Defense Evasion|Persistence,Patchwork|APT41|Leviathan
+T1217,Browser Bookmark Discovery,Discovery,no
+T1213,Data from Information Repositories,Collection,Turla
+T1189,Drive-by Compromise,Initial Access,Turla|Windshift|RTM|Darkhotel|APT38|Dragonfly 2.0|BRONZE BUTLER|Leafminer|Dark Caracal|APT19|APT32|Lazarus Group|Threat Group-3390|Elderwood|APT37|Patchwork|PLATINUM
+T1203,Exploitation for Client Execution,Execution,Sandworm Team|MuddyWater|Frankenstein|Inception|BlackTech|APT41|admin@338|Threat Group-3390|APT12|The White Company|APT33|APT32|APT28|Tropic Trooper|Lazarus Group|BRONZE BUTLER|Cobalt Group|APT37|Patchwork|Leviathan|Elderwood|TA459|APT29
+T1212,Exploitation for Credential Access,Credential Access,no
+T1211,Exploitation for Defense Evasion,Defense Evasion,APT28
+T1190,Exploit Public-Facing Application,Initial Access,Blue Mockingbird|Rocke|APT39|BlackTech|APT41|Soft Cell|Night Dragon|Axiom
+T1210,Exploitation of Remote Services,Lateral Movement,Threat Group-3390|APT28
+T1202,Indirect Command Execution,Defense Evasion,no
+T1200,Hardware Additions,Initial Access,DarkVishnya
T1201,Password Policy Discovery,Discovery,Turla|OilRig
+T1219,Remote Access Software,Command And Control,Sandworm Team|DarkVishnya|RTM|Kimsuky|Night Dragon|Thrip|Cobalt Group|Carbanak
+T1207,Rogue Domain Controller,Defense Evasion,no
+T1199,Trusted Relationship,Initial Access,APT28|menuPass
+T1218,Signed Binary Proxy Execution,Defense Evasion,no
+T1204,User Execution,Execution,no
+T1216,Signed Script Proxy Execution,Defense Evasion,no
T1195,Supply Chain Compromise,Initial Access,Elderwood
T1205,Traffic Signaling,Defense Evasion|Persistence|Command And Control,no
-T1189,Drive-by Compromise,Initial Access,Dragonfly|PROMETHIUM|Turla|Windshift|RTM|Darkhotel|APT38|Lazarus Group|APT32|Dark Caracal|Dragonfly 2.0|BRONZE BUTLER|Leafminer|APT19|Threat Group-3390|APT37|Patchwork|PLATINUM|Elderwood
-T1212,Exploitation for Credential Access,Credential Access,no
-T1219,Remote Access Software,Command And Control,Sandworm Team|DarkVishnya|RTM|Kimsuky|Night Dragon|Thrip|Cobalt Group|Carbanak
-T1211,Exploitation for Defense Evasion,Defense Evasion,APT28
-T1218,Signed Binary Proxy Execution,Defense Evasion,no
-T1216,Signed Script Proxy Execution,Defense Evasion,no
-T1199,Trusted Relationship,Initial Access,GOLD SOUTHFIELD|APT28|menuPass
T1176,Browser Extensions,Persistence,Kimsuky|Stolen Pencil
T1175,Component Object Model and Distributed COM,Lateral Movement|Execution,no
+T1187,Forced Authentication,Credential Access,DarkHydrus|Dragonfly 2.0
T1185,Man in the Browser,Collection,no
-T1187,Forced Authentication,Credential Access,Dragonfly 2.0|DarkHydrus
-T1149,LC_MAIN Hijacking,Defense Evasion,no
+T1134,Access Token Manipulation,Defense Evasion|Privilege Escalation,Blue Mockingbird
T1136,Create Account,Persistence,no
-T1134,Access Token Manipulation,Defense Evasion|Privilege Escalation,FIN6|Blue Mockingbird
-T1135,Network Share Discovery,Discovery,Wizard Spider|APT32|APT39|DarkVishnya|APT41|Tropic Trooper|APT1|Dragonfly 2.0|Sowbug
-T1140,Deobfuscate/Decode Files or Information,Defense Evasion,UNC2452|Rocke|Sandworm Team|Gamaredon Group|Molerats|Frankenstein|Turla|WIRTE|Darkhotel|Tropic Trooper|menuPass|Threat Group-3390|Gorgon Group|APT19|Honeybee|Leviathan|MuddyWater|APT28|OilRig|BRONZE BUTLER
+T1140,Deobfuscate/Decode Files or Information,Defense Evasion,Rocke|Sandworm Team|Gamaredon Group|Molerats|Frankenstein|Turla|WIRTE|Darkhotel|Tropic Trooper|menuPass|Honeybee|Threat Group-3390|APT19|Gorgon Group|Leviathan|MuddyWater|APT28|OilRig|BRONZE BUTLER
+T1149,LC_MAIN Hijacking,Defense Evasion,no
+T1135,Network Share Discovery,Discovery,APT32|APT39|DarkVishnya|APT41|Tropic Trooper|APT1|Dragonfly 2.0|Sowbug
T1137,Office Application Startup,Persistence,Gamaredon Group|APT32
T1153,Source,Execution,no
-T1133,External Remote Services,Persistence|Initial Access,Wizard Spider|GOLD SOUTHFIELD|Chimera|Sandworm Team|APT41|Soft Cell|TEMP.Veles|Night Dragon|Ke3chang|OilRig|Dragonfly 2.0|FIN5|Threat Group-3390|APT18
+T1133,External Remote Services,Persistence|Initial Access,Sandworm Team|APT41|Soft Cell|TEMP.Veles|Night Dragon|OilRig|Dragonfly 2.0|Ke3chang|FIN5|Threat Group-3390|APT18
T1132,Data Encoding,Command And Control,no
T1129,Shared Modules,Execution,no
T1127,Trusted Developer Utilities Proxy Execution,Defense Evasion,no
@@ -471,72 +369,72 @@ T1125,Video Capture,Collection,Silence|FIN7
T1124,System Time Discovery,Discovery,The White Company|Lazarus Group|BRONZE BUTLER|Turla
T1123,Audio Capture,Collection,APT37
T1120,Peripheral Device Discovery,Discovery,Turla|APT37|Gamaredon Group|Equation|APT28
-T1119,Automated Collection,Collection,Gamaredon Group|Tropic Trooper|Frankenstein|APT1|APT28|Patchwork|FIN5|OilRig|Threat Group-3390|FIN6
+T1119,Automated Collection,Collection,Tropic Trooper|Frankenstein|APT1|APT28|Patchwork|OilRig|FIN5|Threat Group-3390|FIN6
T1115,Clipboard Data,Collection,APT39|APT38
T1114,Email Collection,Collection,no
-T1113,Screen Capture,Collection,Gamaredon Group|APT39|Silence|MuddyWater|OilRig|Dragonfly 2.0|FIN7|Dark Caracal|BRONZE BUTLER|Magic Hound|Group5|APT28
-T1112,Modify Registry,Defense Evasion,Lazarus Group|Gamaredon Group|Blue Mockingbird|Wizard Spider|Silence|APT41|Turla|APT32|APT38|Dragonfly 2.0|Patchwork|APT19|Gorgon Group|Threat Group-3390|Honeybee|FIN8
+T1113,Screen Capture,Collection,Gamaredon Group|APT39|Silence|MuddyWater|Dragonfly 2.0|OilRig|Dark Caracal|FIN7|BRONZE BUTLER|Magic Hound|Group5|APT28
+T1112,Modify Registry,Defense Evasion,Gamaredon Group|Blue Mockingbird|Wizard Spider|Silence|APT41|Turla|APT32|APT38|Dragonfly 2.0|APT19|Threat Group-3390|Honeybee|Patchwork|Gorgon Group|FIN8
T1111,Two-Factor Authentication Interception,Credential Access,no
T1110,Brute Force,Credential Access,DarkVishnya|APT39|OilRig|FIN5|Turla
T1108,Redundant Access,Defense Evasion|Persistence,no
-T1106,Native API,Execution,Chimera|Gamaredon Group|Tropic Trooper|Sharpshooter|Turla|Silence|APT37|Gorgon Group
-T1105,Ingress Tool Transfer,Command And Control,UNC2452|Chimera|Sandworm Team|Whitefly|Rocke|APT39|Tropic Trooper|Sharpshooter|Molerats|Frankenstein|Silence|APT-C-36|APT41|Soft Cell|TA505|WIRTE|APT33|MuddyWater|APT18|APT38|Rancor|OilRig|Dragonfly 2.0|Cobalt Group|Turla|Gorgon Group|APT37|Leviathan|Elderwood|PLATINUM|FIN8|Magic Hound|APT32|APT3|BRONZE BUTLER|menuPass|FIN7|Gamaredon Group|Patchwork|Lazarus Group|Threat Group-3390|APT28
+T1106,Native API,Execution,Gamaredon Group|Tropic Trooper|Sharpshooter|Turla|Silence|Gorgon Group|APT37
+T1105,Ingress Tool Transfer,Command And Control,Sandworm Team|Whitefly|Rocke|APT39|Tropic Trooper|Sharpshooter|Molerats|Frankenstein|Silence|APT-C-36|APT41|Soft Cell|TA505|WIRTE|APT33|MuddyWater|APT18|APT38|Rancor|Cobalt Group|Turla|Gorgon Group|OilRig|Dragonfly 2.0|APT37|FIN8|PLATINUM|Leviathan|Elderwood|Magic Hound|APT3|APT32|BRONZE BUTLER|menuPass|FIN7|Gamaredon Group|Patchwork|Lazarus Group|Threat Group-3390|APT28
T1104,Multi-Stage Channels,Command And Control,APT41|MuddyWater|APT3
-T1102,Web Service,Command And Control,Chimera|Gamaredon Group|Rocke|Inception|FIN6
+T1102,Web Service,Command And Control,Gamaredon Group|Rocke|Inception|FIN6
T1098,Account Manipulation,Persistence,APT3|Dragonfly 2.0|Lazarus Group
-T1095,Non-Application Layer Protocol,Command And Control,FIN6|APT29|PLATINUM|APT3
+T1095,Non-Application Layer Protocol,Command And Control,APT29|PLATINUM|APT3
T1092,Communication Through Removable Media,Command And Control,APT28
T1091,Replication Through Removable Media,Lateral Movement|Initial Access,Tropic Trooper|Darkhotel|APT28
-T1090,Proxy,Command And Control,Sandworm Team|Blue Mockingbird|APT41|Turla
-T1087,Account Discovery,Discovery,UNC2452
-T1083,File and Directory Discovery,Discovery,UNC2452|Gamaredon Group|Tropic Trooper|Inception|APT41|Kimsuky|APT32|MuddyWater|APT18|Dragonfly 2.0|Leafminer|Honeybee|Dark Caracal|APT3|BRONZE BUTLER|Sowbug|Magic Hound|APT28|Patchwork|Lazarus Group|Dust Storm|admin@338|Turla|Ke3chang
-T1082,System Information Discovery,Discovery,UNC2452|Wizard Spider|Rocke|Sandworm Team|Blue Mockingbird|Tropic Trooper|Frankenstein|Inception|Kimsuky|Darkhotel|MuddyWater|APT18|APT37|Honeybee|APT19|APT32|Magic Hound|Sowbug|OilRig|APT3|Gamaredon Group|Patchwork|Stealth Falcon|Lazarus Group|admin@338|Turla|Ke3chang
-T1080,Taint Shared Content,Lateral Movement,Gamaredon Group|BRONZE BUTLER|Darkhotel
-T1078,Valid Accounts,Defense Evasion|Persistence|Privilege Escalation|Initial Access,UNC2452|Chimera|Sandworm Team|Wizard Spider|Silence|APT41|Soft Cell|TEMP.Veles|APT39|FIN4|Night Dragon|Dragonfly 2.0|Leviathan|APT33|FIN8|FIN5|OilRig|APT28|FIN10|menuPass|Suckfly|FIN6|Threat Group-3390|APT18|PittyTiger|Carbanak
+T1090,Proxy,Command And Control,Sandworm Team|Blue Mockingbird|Wizard Spider|APT41|Turla
+T1087,Account Discovery,Discovery,no
+T1083,File and Directory Discovery,Discovery,Gamaredon Group|Tropic Trooper|Inception|APT41|Kimsuky|APT32|MuddyWater|APT18|Leafminer|Honeybee|Dark Caracal|Dragonfly 2.0|Magic Hound|Sowbug|BRONZE BUTLER|APT3|APT28|Patchwork|Lazarus Group|Dust Storm|admin@338|Turla|Ke3chang
+T1082,System Information Discovery,Discovery,Rocke|Sandworm Team|Blue Mockingbird|Tropic Trooper|Frankenstein|Inception|Kimsuky|Darkhotel|MuddyWater|APT18|Honeybee|APT19|APT37|APT32|Magic Hound|OilRig|APT3|Sowbug|Gamaredon Group|Patchwork|Stealth Falcon|Lazarus Group|admin@338|Turla|Ke3chang
+T1080,Taint Shared Content,Lateral Movement,BRONZE BUTLER|Darkhotel
+T1078,Valid Accounts,Defense Evasion|Persistence|Privilege Escalation|Initial Access,Sandworm Team|Wizard Spider|Silence|APT41|Soft Cell|TEMP.Veles|APT39|FIN4|Night Dragon|Dragonfly 2.0|FIN8|Leviathan|APT33|OilRig|FIN5|menuPass|APT28|FIN10|Suckfly|FIN6|Threat Group-3390|APT18|PittyTiger|Carbanak
T1074,Data Staged,Collection,Wizard Spider
T1072,Software Deployment Tools,Execution|Lateral Movement,Silence|APT32|Threat Group-1314
T1071,Application Layer Protocol,Command And Control,Rocke|Magic Hound|Dragonfly 2.0
-T1070,Indicator Removal on Host,Defense Evasion,UNC2452
-T1069,Permission Groups Discovery,Discovery,UNC2452|TA505|APT3
+T1070,Indicator Removal on Host,Defense Evasion,no
+T1069,Permission Groups Discovery,Discovery,TA505|APT3
T1068,Exploitation for Privilege Escalation,Privilege Escalation,Whitefly|APT33|Cobalt Group|PLATINUM|FIN8|APT32|Threat Group-3390|FIN6|APT28
T1064,Scripting,Defense Evasion|Execution,no
T1062,Hypervisor,Persistence,no
T1061,Graphical User Interface,Execution,no
-T1059,Command and Scripting Interpreter,Execution,APT32|Molerats|Whitefly|APT39|APT19|FIN7|Dragonfly 2.0|OilRig|FIN5|Stealth Falcon|FIN6|Ke3chang
-T1057,Process Discovery,Discovery,UNC2452|Rocke|Frankenstein|Inception|Darkhotel|MuddyWater|APT1|APT38|Tropic Trooper|APT37|Honeybee|OilRig|APT3|Magic Hound|APT28|Winnti Group|Stealth Falcon|Poseidon Group|Lazarus Group|Molerats|Turla|Deep Panda|Ke3chang
+T1059,Command and Scripting Interpreter,Execution,APT32|Molerats|Whitefly|Dragonfly 2.0|APT19|FIN7|OilRig|FIN5|Stealth Falcon|FIN6|Ke3chang
+T1057,Process Discovery,Discovery,Rocke|Frankenstein|Inception|Darkhotel|MuddyWater|APT1|APT38|Tropic Trooper|APT37|Honeybee|OilRig|APT3|Magic Hound|APT28|Winnti Group|Stealth Falcon|Poseidon Group|Lazarus Group|Molerats|Turla|Deep Panda|Ke3chang
T1056,Input Capture,Collection|Credential Access,no
-T1055,Process Injection,Defense Evasion|Privilege Escalation,APT32|Sharpshooter|Silence|APT41|Kimsuky|Cobalt Group|APT37|Turla|Honeybee|PLATINUM
+T1055,Process Injection,Defense Evasion|Privilege Escalation,APT32|Sharpshooter|Silence|APT41|Kimsuky|Turla|Cobalt Group|APT37|Honeybee|PLATINUM
T1053,Scheduled Task/Job,Execution|Persistence|Privilege Escalation,no
T1052,Exfiltration Over Physical Medium,Exfiltration,no
T1051,Shared Webroot,Lateral Movement,no
-T1049,System Network Connections Discovery,Discovery,Tropic Trooper|APT41|APT38|Soft Cell|APT32|APT1|OilRig|APT3|Threat Group-3390|menuPass|Poseidon Group|admin@338|Turla|Ke3chang
+T1049,System Network Connections Discovery,Discovery,Tropic Trooper|APT41|APT38|Soft Cell|APT32|APT1|OilRig|APT3|menuPass|Threat Group-3390|Poseidon Group|admin@338|Turla|Ke3chang
T1048,Exfiltration Over Alternative Protocol,Exfiltration,no
-T1047,Windows Management Instrumentation,Execution,UNC2452|Chimera|Blue Mockingbird|Wizard Spider|Frankenstein|APT41|FIN6|Soft Cell|APT32|MuddyWater|OilRig|Threat Group-3390|FIN8|Leviathan|menuPass|Stealth Falcon|Lazarus Group|APT29|Deep Panda
-T1046,Network Service Scanning,Discovery,Rocke|DarkVishnya|APT41|Tropic Trooper|APT39|APT32|Cobalt Group|Leafminer|OilRig|menuPass|Suckfly|FIN6|Threat Group-3390
-T1043,Commonly Used Port,Command And Control,OilRig|APT28|TEMP.Veles|Night Dragon|APT29|APT18|FIN7|Dragonfly 2.0|APT19|FIN8|APT37|APT3|Magic Hound|Lazarus Group|Threat Group-3390
+T1047,Windows Management Instrumentation,Execution,Blue Mockingbird|Wizard Spider|Frankenstein|APT41|FIN6|Soft Cell|APT32|MuddyWater|OilRig|Threat Group-3390|FIN8|Leviathan|menuPass|Stealth Falcon|Lazarus Group|APT29|Deep Panda
+T1046,Network Service Scanning,Discovery,Rocke|DarkVishnya|APT41|Tropic Trooper|APT39|APT32|Leafminer|OilRig|Cobalt Group|menuPass|Suckfly|FIN6|Threat Group-3390
+T1043,Commonly Used Port,Command And Control,Machete|OilRig|APT28|TEMP.Veles|Night Dragon|APT29|APT18|APT19|Dragonfly 2.0|FIN7|FIN8|APT37|Magic Hound|APT3|Lazarus Group|Threat Group-3390
T1041,Exfiltration Over C2 Channel,Exfiltration,Sandworm Team|MuddyWater|Wizard Spider|Frankenstein|Kimsuky|Soft Cell|APT32|APT3|Gamaredon Group|Stealth Falcon|Lazarus Group|Ke3chang
T1040,Network Sniffing,Credential Access|Discovery,Sandworm Team|DarkVishnya|APT33|Stolen Pencil|APT28
-T1039,Data from Network Shared Drive,Collection,Gamaredon Group|BRONZE BUTLER|Sowbug|menuPass
+T1039,Data from Network Shared Drive,Collection,Sowbug|BRONZE BUTLER|menuPass
T1037,Boot or Logon Initialization Scripts,Persistence|Privilege Escalation,Rocke
-T1036,Masquerading,Defense Evasion,UNC2452|Windshift|APT32|BRONZE BUTLER|menuPass|Dragonfly 2.0
+T1036,Masquerading,Defense Evasion,Windshift|APT32|BRONZE BUTLER|menuPass|Dragonfly 2.0
T1034,Path Interception,Persistence|Privilege Escalation,no
-T1033,System Owner/User Discovery,Discovery,Wizard Spider|Frankenstein|APT41|Soft Cell|Tropic Trooper|APT39|MuddyWater|APT32|APT37|APT19|Dragonfly 2.0|OilRig|Magic Hound|FIN10|Gamaredon Group|Patchwork|Stealth Falcon|Lazarus Group|APT3
+T1033,System Owner/User Discovery,Discovery,Frankenstein|APT41|Soft Cell|Tropic Trooper|APT39|MuddyWater|APT32|APT37|APT19|Dragonfly 2.0|OilRig|Magic Hound|FIN10|Gamaredon Group|Patchwork|Stealth Falcon|Lazarus Group|APT3
T1030,Data Transfer Size Limits,Exfiltration,Threat Group-3390
T1029,Scheduled Transfer,Exfiltration,no
-T1027,Obfuscated Files or Information,Defense Evasion,UNC2452|FIN6|Chimera|Gamaredon Group|Rocke|Sandworm Team|Blue Mockingbird|Whitefly|Molerats|Wizard Spider|Mofang|Frankenstein|Inception|APT-C-36|APT41|Machete|Soft Cell|Turla|TA505|Silence|APT33|Night Dragon|Darkhotel|Gallmaker|APT29|APT18|Tropic Trooper|menuPass|Honeybee|Patchwork|Threat Group-3390|APT19|Cobalt Group|Leafminer|APT37|Dark Caracal|FIN8|MuddyWater|FIN7|BlackOasis|Leviathan|Elderwood|OilRig|Magic Hound|APT3|APT32|Group5|Lazarus Group|Dust Storm|Putter Panda|APT28
+T1027,Obfuscated Files or Information,Defense Evasion,Gamaredon Group|Rocke|Sandworm Team|Blue Mockingbird|Whitefly|Molerats|Wizard Spider|Mofang|Frankenstein|Inception|APT-C-36|APT41|Machete|Soft Cell|Turla|TA505|Silence|APT33|Night Dragon|Darkhotel|Gallmaker|APT29|APT18|Tropic Trooper|Cobalt Group|Patchwork|Leafminer|APT37|Threat Group-3390|Honeybee|Dark Caracal|menuPass|APT19|BlackOasis|FIN8|Leviathan|Elderwood|MuddyWater|FIN7|Magic Hound|OilRig|APT3|APT32|Group5|Dust Storm|Lazarus Group|Putter Panda|APT28
T1026,Multiband Communication,Command And Control,Lazarus Group
T1025,Data from Removable Media,Collection,Machete|Turla|Gamaredon Group|APT28
T1021,Remote Services,Lateral Movement,no
-T1020,Automated Exfiltration,Exfiltration,Gamaredon Group|Tropic Trooper|Frankenstein|Honeybee
-T1018,Remote System Discovery,Discovery,UNC2452|Sandworm Team|Rocke|Wizard Spider|Silence|Soft Cell|APT39|APT32|Threat Group-3390|Dragonfly 2.0|Ke3chang|Leafminer|Deep Panda|FIN8|FIN5|APT3|BRONZE BUTLER|menuPass|FIN6|Turla
-T1016,System Network Configuration Discovery,Discovery,Wizard Spider|Sandworm Team|Tropic Trooper|Frankenstein|APT41|Soft Cell|APT32|Darkhotel|MuddyWater|APT1|Dragonfly 2.0|APT19|OilRig|Magic Hound|menuPass|Threat Group-3390|Stealth Falcon|Lazarus Group|APT3|Naikon|admin@338|Turla|Ke3chang
+T1020,Automated Exfiltration,Exfiltration,Tropic Trooper|Frankenstein|Honeybee
+T1018,Remote System Discovery,Discovery,Sandworm Team|Rocke|Wizard Spider|Silence|Soft Cell|APT39|APT32|Deep Panda|Threat Group-3390|Dragonfly 2.0|Leafminer|Ke3chang|FIN8|APT3|FIN5|BRONZE BUTLER|menuPass|FIN6|Turla
+T1016,System Network Configuration Discovery,Discovery,Sandworm Team|Tropic Trooper|Frankenstein|APT41|Soft Cell|APT32|Darkhotel|MuddyWater|APT1|APT19|Dragonfly 2.0|Magic Hound|OilRig|menuPass|Threat Group-3390|Stealth Falcon|Lazarus Group|APT3|Naikon|admin@338|Turla|Ke3chang
T1014,Rootkit,Defense Evasion,Rocke|APT41|APT28|Winnti Group
-T1012,Query Registry,Discovery,APT32|Threat Group-3390|Dragonfly 2.0|OilRig|Stealth Falcon|Lazarus Group|Turla
+T1012,Query Registry,Discovery,APT32|Dragonfly 2.0|Threat Group-3390|OilRig|Stealth Falcon|Lazarus Group|Turla
T1011,Exfiltration Over Other Network Medium,Exfiltration,no
T1010,Application Window Discovery,Discovery,Lazarus Group
T1008,Fallback Channels,Command And Control,APT41|OilRig|Lazarus Group
T1007,System Service Discovery,Discovery,BRONZE BUTLER|APT1|OilRig|Poseidon Group|admin@338|Turla|Ke3chang
T1006,Direct Volume Access,Defense Evasion,no
-T1005,Data from Local System,Collection,UNC2452|FIN6|Gamaredon Group|APT39|Frankenstein|Inception|Kimsuky|Soft Cell|Turla|menuPass|Dark Caracal|Dragonfly 2.0|Honeybee|APT28|APT37|APT3|BRONZE BUTLER|Patchwork|Stealth Falcon|Lazarus Group|Dust Storm|Threat Group-3390|APT1|Ke3chang
+T1005,Data from Local System,Collection,Gamaredon Group|APT39|Frankenstein|Inception|Kimsuky|Soft Cell|Turla|menuPass|Dark Caracal|Dragonfly 2.0|Honeybee|APT37|APT28|APT3|BRONZE BUTLER|Patchwork|Stealth Falcon|Lazarus Group|Dust Storm|Threat Group-3390|APT1|Ke3chang
T1003,OS Credential Dumping,Credential Access,APT39|Frankenstein|APT32|APT28|Leviathan|Sowbug|Suckfly|Poseidon Group|Axiom
T1001,Data Obfuscation,Command And Control,Axiom
diff --git a/docs/detections.md b/docs/detections.md
index 683842d002..26234bbc52 100644
--- a/docs/detections.md
+++ b/docs/detections.md
@@ -47,6 +47,26 @@ All the detections shipped to different Splunk products. Below is a breakdown by
+- [AWS IAM AccessDenied Discovery Events](#aws-iam-accessdenied-discovery-events)
+
+
+
+- [AWS IAM Assume Role Policy Brute Force](#aws-iam-assume-role-policy-brute-force)
+
+
+
+- [AWS IAM Delete Policy](#aws-iam-delete-policy)
+
+
+
+- [AWS IAM Failure Group Deletion](#aws-iam-failure-group-deletion)
+
+
+
+- [AWS IAM Successful Group Deletion](#aws-iam-successful-group-deletion)
+
+
+
- [AWS Network Access Control List Created with All Open Ports](#aws-network-access-control-list-created-with-all-open-ports)
@@ -144,8 +164,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by
-
-
@@ -250,6 +268,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by
+
+
@@ -508,6 +528,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by
+
+
+
+
@@ -531,6 +555,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by
+
+
- [High Number of Login Failures from a single source](#high-number-of-login-failures-from-a-single-source)
@@ -928,6 +954,34 @@ All the detections shipped to different Splunk products. Below is a breakdown by
+
+
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@@ -1019,6 +1073,16 @@ All the detections shipped to different Splunk products. Below is a breakdown by
+
+
+
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+
@@ -1062,10 +1126,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by
-- [Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass](#attempt-to-set-default-powershell-execution-policy-to-unrestricted-or-bypass)
-
-
-
- [Attempt To Stop Security Service](#attempt-to-stop-security-service)
@@ -1234,6 +1294,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by
+- [DLLHost with no Command Line Arguments with Network](#dllhost-with-no-command-line-arguments-with-network)
+
+
+
@@ -1358,9 +1422,9 @@ All the detections shipped to different Splunk products. Below is a breakdown by
-- [Detect Oulook exe writing a zip file](#detect-oulook-exe-writing-a--zip-file)
+- [Detect Outlook exe writing a zip file](#detect-outlook-exe-writing-a-zip-file)
@@ -1582,6 +1646,14 @@ All the detections shipped to different Splunk products. Below is a breakdown by
+- [Excel Spawning PowerShell](#excel-spawning-powershell)
+
+
+
+- [Excel Spawning Windows Script Host](#excel-spawning-windows-script-host)
+
+
+
@@ -1626,6 +1698,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by
+- [GPUpdate with no Command Line Arguments with Network](#gpupdate-with-no-command-line-arguments-with-network)
+
+
+
- [Hiding Files And Directories With Attrib exe](#hiding-files-and-directories-with-attrib-exe)
@@ -1805,6 +1881,26 @@ All the detections shipped to different Splunk products. Below is a breakdown by
+
+
+
+- [Office Application Spawn rundll32 process](#office-application-spawn-rundll32-process)
+
+
+
+- [Office Document Creating Schedule Task](#office-document-creating-schedule-task)
+
+
+
+- [Office Document Executing Macro Code](#office-document-executing-macro-code)
+
+
+
+- [Office Document Spawned Child Process To Download](#office-document-spawned-child-process-to-download)
+
+
+
+- [Office Product Spawning Rundll32 with no DLL](#office-product-spawning-rundll32-with-no-dll)
@@ -1866,8 +1962,6 @@ All the detections shipped to different Splunk products. Below is a breakdown by
-- [Rare Parent-Child Process Relationship](#rare-parent-child-process-relationship)
-
- [Reconnaissance and Access to Accounts Groups and Policies via PowerSploit modules](#reconnaissance-and-access-to-accounts-groups-and-policies-via-powersploit-modules)
@@ -1972,6 +2066,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by
+- [Rundll32 with no Command Line Arguments with Network](#rundll32-with-no-command-line-arguments-with-network)
+
+
+
- [Ryuk Test Files Detected](#ryuk-test-files-detected)
@@ -2012,6 +2110,14 @@ All the detections shipped to different Splunk products. Below is a breakdown by
+- [SearchProtocolHost with no Command Line with Network](#searchprotocolhost-with-no-command-line-with-network)
+
+
+
+- [Set Default PowerShell Execution Policy To Unrestricted or Bypass](#set-default-powershell-execution-policy-to-unrestricted-or-bypass)
+
+
+
- [Setting Credentials via DSInternals modules](#setting-credentials-via-dsinternals-modules)
@@ -2024,6 +2130,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by
+- [Shedule Task with HTTP Command Arguments](#shedule-task-with-http-command-arguments)
+
+
+
- [Shim Database File Creation](#shim-database-file-creation)
@@ -2232,6 +2342,14 @@ All the detections shipped to different Splunk products. Below is a breakdown by
+- [WinEvent Scheduled Task Created Within Public Path](#winevent-scheduled-task-created-within-public-path)
+
+
+
+- [WinEvent Scheduled Task Created to Spawn Shell](#winevent-scheduled-task-created-to-spawn-shell)
+
+
+
- [Windows AdFind Exe](#windows-adfind-exe)
@@ -2252,6 +2370,18 @@ All the detections shipped to different Splunk products. Below is a breakdown by
+- [Winword Spawning Cmd](#winword-spawning-cmd)
+
+
+
+- [Winword Spawning PowerShell](#winword-spawning-powershell)
+
+
+
+- [Winword Spawning Windows Script Host](#winword-spawning-windows-script-host)
+
+
+
@@ -2428,6 +2558,16 @@ All the detections shipped to different Splunk products. Below is a breakdown by
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-
@@ -2543,6 +2681,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by
+
+
- [Detect Port Security Violation](#detect-port-security-violation)
@@ -2696,6 +2836,10 @@ All the detections shipped to different Splunk products. Below is a breakdown by
+
+
+
+
@@ -2740,6 +2884,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by
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@@ -2890,6 +3036,16 @@ All the detections shipped to different Splunk products. Below is a breakdown by
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@@ -2982,6 +3138,8 @@ All the detections shipped to different Splunk products. Below is a breakdown by
+
+
- [SMB Traffic Spike](#smb-traffic-spike)
@@ -3088,6 +3246,12 @@ All the detections shipped to different Splunk products. Below is a breakdown by
+
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@@ -3151,6 +3315,16 @@ All the detections shipped to different Splunk products. Below is a breakdown by
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@@ -3403,6 +3577,16 @@ All the detections shipped to different Splunk products. Below is a breakdown by
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@@ -3691,6 +3875,12 @@ All the detections shipped to different Splunk products. Below is a breakdown by
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@@ -4246,6 +4464,16 @@ All the detections shipped to different Splunk products. Below is a breakdown by
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@@ -4564,6 +4792,12 @@ All the detections shipped to different Splunk products. Below is a breakdown by
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@@ -4704,6 +4938,18 @@ All the detections shipped to different Splunk products. Below is a breakdown by
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@@ -4750,6 +4996,12 @@ All the detections shipped to different Splunk products. Below is a breakdown by
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@@ -4877,6 +5129,16 @@ All the detections shipped to different Splunk products. Below is a breakdown by
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@@ -5243,11 +5505,13 @@ While this search has no known false positives, it is possible that an AWS admin
#### Reference
+
* https://labs.bishopfox.com/tech-blog/privilege-escalation-in-aws
* https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_create_policy_version/aws_cloudtrail_events.json
@@ -5317,11 +5581,13 @@ While this search has no known false positives, it is possible that an AWS admin
#### Reference
+
* https://labs.bishopfox.com/tech-blog/privilege-escalation-in-aws
* https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_createaccesskey/aws_cloudtrail_events.json
@@ -5395,11 +5661,13 @@ While this search has no known false positives, it is possible that an AWS admin
#### Reference
+
* https://labs.bishopfox.com/tech-blog/privilege-escalation-in-aws
* https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_createloginprofile/aws_cloudtrail_events.json
@@ -5549,6 +5817,7 @@ unknown
#### Reference
+
* https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/
* https://github.com/d1vious/git-wild-hunt
@@ -5556,6 +5825,7 @@ unknown
* https://www.youtube.com/watch?v=PgzNib37g0M
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/aws_kms_key/aws_cloudtrail_events.json
@@ -5629,6 +5899,7 @@ bucket with S3 encryption
#### Reference
+
* https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/
* https://github.com/d1vious/git-wild-hunt
@@ -5636,6 +5907,7 @@ bucket with S3 encryption
* https://www.youtube.com/watch?v=PgzNib37g0M
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/s3_file_encryption/aws_cloudtrail_events.json
@@ -5759,14 +6031,398 @@ While this search has no known false positives.
#### Reference
+
* https://github.com/aquasecurity/cloudsploit
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1526/aws_security_scanner/aws_security_scanner.json
+_version_: 1
+
+
+---
+
+### AWS IAM AccessDenied Discovery Events
+The following detection identifies excessive AccessDenied events within an hour timeframe. It is possible that an access key to AWS may have been stolen and is being misused to perform discovery events. In these instances, the access is not available with the key stolen therefore these events will be generated.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud, Splunk Security Analytics for AWS
+- **Datamodel**:
+- **ATT&CK**: [T1580](https://attack.mitre.org/techniques/T1580/)
+- **Last Updated**: 2021-04-05
+
+
+ details
+
+#### Search
+```
+`cloudtrail` (errorCode = "AccessDenied") user_type=IAMUser (userAgent!=*.amazonaws.com)
+| bucket _time span=1h
+| stats count as failures min(_time) as firstTime max(_time) as lastTime, dc(eventName) as methods, dc(eventSource) as sources values(userIdentity.arn) by src_ip, userIdentity.arn, _time
+| where failures >= 5 and methods >= 1 and sources >= 1
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `aws_iam_accessdenied_discovery_events_filter`
+```
+#### Associated Analytic Story
+
+* Suspicious Cloud User Activities
+
+
+#### How To Implement
+The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
+
+#### Required field
+
+* _time
+
+* eventName
+
+* eventSource
+
+* userAgent
+
+* errorCode
+
+* userIdentity.type
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1580 | Cloud Infrastructure Discovery | Discovery |
+
+
+#### Kill Chain Phase
+
+* Reconnaissance
+
+
+#### Known False Positives
+It is possible to start this detection will need to be tuned by source IP or user. In addition, change the count values to an upper threshold to restrict false positives.
+
+#### Reference
+
+
+* https://aws.amazon.com/premiumsupport/knowledge-center/troubleshoot-iam-permission-errors/
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_iam_accessdenied_discovery_events/aws_iam_accessdenied_discovery_events.json
+
+
+_version_: 1
+
+
+---
+
+### AWS IAM Assume Role Policy Brute Force
+The following detection identifies any malformed policy document exceptions with a status of `failure`. A malformed policy document exception occurs in instances where roles are attempted to be assumed, or brute forced. In a brute force attempt, using a tool like CloudSploit or Pacu, an attempt will look like `arn:aws:iam::111111111111:role/aws-service-role/rds.amazonaws.com/AWSServiceRoleForRDS`. Meaning, when an adversary is attempting to identify a role name, multiple failures will occur. This detection focuses on the errors of a remote attempt that is failing.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud, Splunk Security Analytics for AWS
+- **Datamodel**:
+- **ATT&CK**: [T1580](https://attack.mitre.org/techniques/T1580/), [T1110](https://attack.mitre.org/techniques/T1110/)
+- **Last Updated**: 2021-04-01
+
+
+ details
+
+#### Search
+```
+`cloudtrail` (errorCode=MalformedPolicyDocumentException) status=failure (userAgent!=*.amazonaws.com)
+| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyName) as policy_name by src eventName eventSource aws_account_id errorCode requestParameters.policyDocument userAgent eventID awsRegion userIdentity.principalId user_arn
+| where count >= 2
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `aws_iam_assume_role_policy_brute_force_filter`
+```
+#### Associated Analytic Story
+
+* AWS IAM Privilege Escalation
+
+
+#### How To Implement
+The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs. Set the `where count` greater than a value to identify suspicious activity in your environment.
+
+#### Required field
+
+* _time
+
+* eventName
+
+* userAgent
+
+* errorCode
+
+* requestParameters.policyName
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1580 | Cloud Infrastructure Discovery | Discovery |
+| T1110 | Brute Force | Credential Access |
+
+
+#### Kill Chain Phase
+
+* Reconnaissance
+
+
+#### Known False Positives
+This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users.
+
+#### Reference
+
+
+* https://www.praetorian.com/blog/aws-iam-assume-role-vulnerabilities
+
+* https://rhinosecuritylabs.com/aws/assume-worst-aws-assume-role-enumeration/
+
+* https://www.elastic.co/guide/en/security/current/aws-iam-brute-force-of-assume-role-policy.html
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_iam_assume_role_policy_brute_force/aws_iam_assume_role_policy_brute_force.json
+
+
+_version_: 1
+
+
+---
+
+### AWS IAM Delete Policy
+The following detection identifes when a policy is deleted on AWS. This does not identify whether successful or failed, but the error messages tell a story of suspicious attempts. There is a specific process to follow when deleting a policy. First, detach the policy from all users, groups, and roles that the policy is attached to, using DetachUserPolicy , DetachGroupPolicy , or DetachRolePolicy.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud, Splunk Security Analytics for AWS
+- **Datamodel**:
+- **ATT&CK**: [T1098](https://attack.mitre.org/techniques/T1098/)
+- **Last Updated**: 2021-04-01
+
+
+ details
+
+#### Search
+```
+`cloudtrail` eventName=DeletePolicy (userAgent!=*.amazonaws.com)
+| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyArn) as policyArn by src eventName eventSource aws_account_id errorCode errorMessage userAgent eventID awsRegion userIdentity.principalId userIdentity.arn
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `aws_iam_delete_policy_filter`
+```
+#### Associated Analytic Story
+
+* AWS IAM Privilege Escalation
+
+
+#### How To Implement
+The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
+
+#### Required field
+
+* _time
+
+* eventName
+
+* userAgent
+
+* errorCode
+
+* requestParameters.policyArn
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1098 | Account Manipulation | Persistence |
+
+
+#### Kill Chain Phase
+
+* Actions on Objectives
+
+
+#### Known False Positives
+This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete policies (least privilege). In addition, this may be saved seperately and tuned for failed or success attempts only.
+
+#### Reference
+
+
+* https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeletePolicy.html
+
+* https://docs.aws.amazon.com/cli/latest/reference/iam/delete-policy.html
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_delete_policy/aws_iam_delete_policy.json
+
+
+_version_: 1
+
+
+---
+
+### AWS IAM Failure Group Deletion
+This detection identifies failure attempts to delete groups. We want to identify when a group is attempting to be deleted, but either access is denied, there is a conflict or there is no group. This is indicative of administrators performing an action, but also could be suspicious behavior occurring. Review parallel IAM events - recently added users, new groups and so forth.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud, Splunk Security Analytics for AWS
+- **Datamodel**:
+- **ATT&CK**: [T1098](https://attack.mitre.org/techniques/T1098/)
+- **Last Updated**: 2021-04-01
+
+
+ details
+
+#### Search
+```
+`cloudtrail` eventSource=iam.amazonaws.com eventName=DeleteGroup errorCode IN (NoSuchEntityException,DeleteConflictException, AccessDenied) (userAgent!=*.amazonaws.com)
+| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.groupName) as group_name by src eventName eventSource aws_account_id errorCode errorMessage userAgent eventID awsRegion userIdentity.principalId user_arn
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `aws_iam_failure_group_deletion_filter`
+```
+#### Associated Analytic Story
+
+* AWS IAM Privilege Escalation
+
+
+#### How To Implement
+The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
+
+#### Required field
+
+* _time
+
+* eventName
+
+* userAgent
+
+* errorCode
+
+* requestParameters.groupName
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1098 | Account Manipulation | Persistence |
+
+
+#### Kill Chain Phase
+
+* Actions on Objectives
+
+
+#### Known False Positives
+This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege).
+
+#### Reference
+
+
+* https://awscli.amazonaws.com/v2/documentation/api/latest/reference/iam/delete-group.html
+
+* https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeleteGroup.html
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_failure_group_deletion/aws_iam_failure_group_deletion.json
+
+
+_version_: 1
+
+
+---
+
+### AWS IAM Successful Group Deletion
+The following query uses IAM events to track the success of a group being deleted on AWS. This is typically not indicative of malicious behavior, but a precurser to additional events thay may unfold. Review parallel IAM events - recently added users, new groups and so forth. Inversely, review failed attempts in a similar manner.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud, Splunk Security Analytics for AWS
+- **Datamodel**:
+- **ATT&CK**: [T1069.003](https://attack.mitre.org/techniques/T1069.003/), [T1098](https://attack.mitre.org/techniques/T1098/)
+- **Last Updated**: 2021-03-31
+
+
+ details
+
+#### Search
+```
+`cloudtrail` eventSource=iam.amazonaws.com eventName=DeleteGroup errorCode=success (userAgent!=*.amazonaws.com)
+| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.groupName) by src eventName eventSource errorCode user_agent awsRegion userIdentity.principalId user_arn
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `aws_iam_successful_group_deletion_filter`
+```
+#### Associated Analytic Story
+
+* AWS IAM Privilege Escalation
+
+
+#### How To Implement
+The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
+
+#### Required field
+
+* _time
+
+* eventName
+
+* userAgent
+
+* errorCode
+
+* requestParameters.groupName
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1069.003 | Cloud Groups | Discovery |
+| T1098 | Account Manipulation | Persistence |
+
+
+#### Kill Chain Phase
+
+* Actions on Objectives
+
+
+#### Known False Positives
+This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege).
+
+#### Reference
+
+
+* https://awscli.amazonaws.com/v2/documentation/api/latest/reference/iam/delete-group.html
+
+* https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeleteGroup.html
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_successful_group_deletion/aws_iam_successful_group_deletion.json
+
+
_version_: 1
@@ -5995,6 +6651,7 @@ Attacks using a Golden SAML or SAML assertion hijacks or forgeries are very diff
#### Reference
+
* https://us-cert.cisa.gov/ncas/alerts/aa21-008a
* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html
@@ -6004,6 +6661,7 @@ Attacks using a Golden SAML or SAML assertion hijacks or forgeries are very diff
* https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/assume_role_with_saml/assume_role_with_saml.json
@@ -6076,6 +6734,7 @@ Updating a SAML provider or creating a new one may not necessarily be malicious
#### Reference
+
* https://us-cert.cisa.gov/ncas/alerts/aa21-008a
* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html
@@ -6085,6 +6744,7 @@ Updating a SAML provider or creating a new one may not necessarily be malicious
* https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/update_saml_provider/update_saml_provider.json
@@ -6155,11 +6815,13 @@ While this search has no known false positives, it is possible that an AWS admin
#### Reference
+
* https://labs.bishopfox.com/tech-blog/privilege-escalation-in-aws
* https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_setdefaultpolicyversion/aws_cloudtrail_events.json
@@ -6229,11 +6891,13 @@ While this search has no known false positives, it is possible that an AWS admin
#### Reference
+
* https://labs.bishopfox.com/tech-blog/privilege-escalation-in-aws
* https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_updateloginprofile/aws_cloudtrail_events.json
@@ -6907,9 +7571,11 @@ Administrators can create memory dumps for debugging purposes, but memory dumps
#### Reference
+
* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
@@ -7151,6 +7817,7 @@ False positives may be present and filtering will need to occur by parent proces
#### Reference
+
* https://docs.microsoft.com/en-us/dotnet/api/system.net.webclient.downloadfile?view=net-5.0
* https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/
@@ -7158,6 +7825,7 @@ False positives may be present and filtering will need to occur by parent proces
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log
@@ -7239,6 +7907,7 @@ False positives may be present and filtering will need to occur by parent proces
#### Reference
+
* https://docs.microsoft.com/en-us/dotnet/api/system.net.webclient.downloadstring?view=net-5.0
* https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/
@@ -7246,6 +7915,7 @@ False positives may be present and filtering will need to occur by parent proces
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log
@@ -7325,11 +7995,13 @@ None identified.
#### Reference
+
* https://github.com/gentilkiwi/mimikatz
* https://adsecurity.org/?p=1275
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/applying_stolen_credentials/logAllMimikatzModules.log
@@ -7409,9 +8081,11 @@ None identified.
#### Reference
+
* https://github.com/PowerShellMafia/PowerSploit
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/applying_stolen_credentials/logAllPowerSploitModulesWithOldNames.log
@@ -7486,9 +8160,11 @@ None identified.
#### Reference
+
* https://github.com/MichaelGrafnetter/DSInternals
+
#### Test Dataset
@@ -7564,82 +8240,6 @@ There may be legitimate reasons for administrators to add a certificate to the u
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.004/atomic_red_team/windows-sysmon.log
-_version_: 6
-
-
----
-
-### Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass
-Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts.
-
-- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
-- **Datamodel**: Endpoint
-- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059.001/)
-- **Last Updated**: 2020-11-06
-
-
- details
-
-#### Search
-```
-
-| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_key_name=ExecutionPolicy (Registry.registry_value_name=Unrestricted OR Registry.registry_value_name=Bypass) by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest
-| `drop_dm_object_name(Registry)`
-| `security_content_ctime(firstTime)`
-|`security_content_ctime(lastTime)`
-| `attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter`
-```
-#### Associated Analytic Story
-
-* Malicious PowerShell
-
-* Credential Dumping
-
-* HAFNIUM Group
-
-
-#### How To Implement
-You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Registry node. You must also be ingesting logs with the fields registry_path, registry_key_name, and registry_value_name from your endpoints.
-
-#### Required field
-
-* _time
-
-* Registry.registry_path
-
-* Registry.registry_key_name
-
-* Registry.registry_value_name
-
-* Registry.dest
-
-
-
-#### ATT&CK
-
-| ID | Technique | Tactic |
-| ----------- | ----------- |--------------|
-| T1059.001 | PowerShell | Execution |
-
-
-#### Kill Chain Phase
-
-* Installation
-
-* Actions on Objectives
-
-
-#### Known False Positives
-Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate.
-
-#### Reference
-
-
-#### Test Dataset
-
-* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_execution_policy/windows-sysmon.log
-
-
_version_: 6
@@ -7848,9 +8448,11 @@ None identified.
#### Reference
+
* https://github.com/splunk/security_content/blob/55a17c65f9f56c2220000b62701765422b46125d/detections/attempted_credential_dump_from_registry_via_reg_exe.yml
+
#### Test Dataset
@@ -7922,9 +8524,11 @@ Administrators may modify the boot configuration.
#### Reference
+
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md#atomic-test-4---windows---disable-windows-recovery-console-repair
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log
@@ -7996,6 +8600,7 @@ Limited false positives will be present. Typically, applications will use `BitsA
#### Reference
+
* https://attack.mitre.org/techniques/T1197/
* https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/bitsadmin
@@ -8005,6 +8610,7 @@ Limited false positives will be present. Typically, applications will use `BitsA
* https://lolbas-project.github.io/lolbas/Binaries/Bitsadmin/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log
@@ -8079,6 +8685,7 @@ Limited false positives, however it may be required to filter based on parent pr
#### Reference
+
* https://github.com/redcanaryco/atomic-red-team/blob/8eb52117b748d378325f7719554a896e37bccec7/atomics/T1105/T1105.md#atomic-test-9---windows---bitsadmin-bits-download
* https://github.com/redcanaryco/atomic-red-team/blob/bc705cb7aaa5f26f2d96585fac8e4c7052df0ff9/atomics/T1197/T1197.md
@@ -8088,6 +8695,7 @@ Limited false positives, however it may be required to filter based on parent pr
* https://thedfirreport.com/2021/03/29/sodinokibi-aka-revil-ransomware/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log
@@ -8231,6 +8839,7 @@ Limited false positives in most environments, however tune as needed based on pa
#### Reference
+
* https://attack.mitre.org/techniques/T1105/
* https://www.avira.com/en/blog/certutil-abused-by-attackers-to-spread-threats
@@ -8238,6 +8847,7 @@ Limited false positives in most environments, however tune as needed based on pa
* https://www.fireeye.com/blog/threat-research/2019/10/certutil-qualms-they-came-to-drop-fombs.html
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/windows-sysmon.log
@@ -8309,6 +8919,7 @@ Limited false positives in most environments, however tune as needed based on pa
#### Reference
+
* https://attack.mitre.org/techniques/T1105/
* https://www.hexacorn.com/blog/2020/08/23/certutil-one-more-gui-lolbin/
@@ -8318,6 +8929,7 @@ Limited false positives in most environments, however tune as needed based on pa
* https://www.avira.com/en/blog/certutil-abused-by-attackers-to-spread-threats
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/windows-sysmon.log
@@ -8389,6 +9001,7 @@ Typically seen used to `encode` files, but it is possible to see legitimate use
#### Reference
+
* https://attack.mitre.org/techniques/T1140/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1140/T1140.md
@@ -8398,6 +9011,7 @@ Typically seen used to `encode` files, but it is possible to see legitimate use
* https://www.bleepingcomputer.com/news/security/certutilexe-could-allow-attackers-to-download-malware-while-bypassing-av/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1140/atomic_red_team/windows-sysmon.log
@@ -8684,11 +9298,13 @@ Operators can execute third party tools using these parameters.
#### Reference
+
* https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html
* https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_b/windows-sysmon.log
@@ -8761,11 +9377,13 @@ unknown
#### Reference
+
* https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html
* https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-system.log
@@ -9667,6 +10285,7 @@ The idea of using named pipes with Cobalt Strike is to blend in. Therefore, some
#### Reference
+
* https://attack.mitre.org/techniques/T1218/009/
* https://docs.microsoft.com/en-us/windows/win32/ipc/named-pipes
@@ -9678,6 +10297,7 @@ The idea of using named pipes with Cobalt Strike is to blend in. Therefore, some
* https://gist.github.com/MHaggis/6c600e524045a6d49c35291a21e10752
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
@@ -9910,9 +10530,11 @@ Other tools can access LSASS for legitimate reasons and generate an event. In th
#### Reference
+
* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
@@ -9984,11 +10606,13 @@ unknown
#### Reference
+
* https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html
* https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-system.log
@@ -10137,9 +10761,11 @@ Administrators often leverage net.exe to create or delete network shares. You sh
#### Reference
+
* https://attack.mitre.org/techniques/T1070/005
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.005/atomic_red_team/windows-sysmon.log
@@ -10215,9 +10841,11 @@ Legitimate administrator usage of Vssadmin or Wmic will create false positives.
#### Reference
+
* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log
@@ -10287,9 +10915,11 @@ Legtimate administrator usage of wmic to create a shadow copy.
#### Reference
+
* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log
@@ -10361,6 +10991,7 @@ Administrators can create memory dumps for debugging purposes, but memory dumps
#### Reference
+
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-5---dump-lsassexe-memory-using-windows-task-manager
* https://attack.mitre.org/techniques/T1003/001/
@@ -10368,6 +10999,7 @@ Administrators can create memory dumps for debugging purposes, but memory dumps
* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
@@ -10443,9 +11075,11 @@ unknown
#### Reference
+
* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log
@@ -10521,9 +11155,11 @@ unknown
#### Reference
+
* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log
@@ -10826,9 +11462,11 @@ None identified.
#### Reference
+
* https://github.com/MichaelGrafnetter/DSInternals
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logAllDSInternalsModules.log
@@ -10906,9 +11544,11 @@ None identified.
#### Reference
+
* https://github.com/MichaelGrafnetter/DSInternals
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logAllDSInternalsModules.log
@@ -10980,9 +11620,11 @@ None identified.
#### Reference
+
* https://github.com/gentilkiwi/mimikatz
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logAllMimikatzModules.log
@@ -11054,9 +11696,11 @@ None identified.
#### Reference
+
* https://github.com/PowerShellMafia/PowerSploit
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logAllPowerSploitModulesWithOldNames.log
@@ -11132,9 +11776,11 @@ Although unlikely, using debuggers this way may be indicative of developers anal
#### Reference
+
* https://medium.com/@clermont1050/covid-19-cyber-infection-c615ead7c29
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logLiveKDFullKernelDump.log
@@ -11287,6 +11933,92 @@ None identified.
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logPowerShellModule.log
+_version_: 1
+
+
+---
+
+### DLLHost with no Command Line Arguments with Network
+The following analytic identifies DLLHost.exe with no command line arguments with a network connection. It is unusual for DLLHost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. DLLHost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
+- **Datamodel**: Endpoint
+- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/)
+- **Last Updated**: 2021-04-19
+
+
+ details
+
+#### Search
+```
+
+| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=dllhost.exe by _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name
+| `drop_dm_object_name(Processes)`
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| regex process="(dllhost\.exe.{0,4}$)"
+| join process_id [
+| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port
+| `drop_dm_object_name(Ports)`
+| rename dest as connection_to_CNC]
+| table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port
+| `dllhost_with_no_command_line_arguments_with_network_filter`
+```
+#### Associated Analytic Story
+
+* Cobalt Strike
+
+
+#### How To Implement
+To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `port` node.
+
+#### Required field
+
+* _time
+
+* EventID
+
+* process_name
+
+* process_id
+
+* parent_process_name
+
+* dest_port
+
+* process_path
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1055 | Process Injection | Defense Evasion, Privilege Escalation |
+
+
+#### Kill Chain Phase
+
+* Exploitation
+
+
+#### Known False Positives
+Although unlikely, some legitimate third party applications may use a moved copy of dllhost, triggering a false positive.
+
+#### Reference
+
+
+* https://raw.githubusercontent.com/threatexpress/malleable-c2/c3385e481159a759f79b8acfe11acf240893b830/jquery-c2.4.2.profile
+
+* https://blog.cobaltstrike.com/2021/02/09/learn-pipe-fitting-for-all-of-your-offense-projects/
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon_dllhost.log
+
+
_version_: 1
@@ -11671,6 +12403,7 @@ Limited false positives. If there is a true false positive, filter based on comm
#### Reference
+
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1482/T1482.md
* http://www.harmj0y.net/blog/redteaming/a-guide-to-attacking-domain-trusts/
@@ -11680,6 +12413,7 @@ Limited false positives. If there is a true false positive, filter based on comm
* https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc754232(v=ws.11)
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/atomic_red_team/windows-sysmon.log
@@ -12428,9 +13162,11 @@ unknown
#### Reference
+
* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit
+
#### Test Dataset
@@ -12491,9 +13227,11 @@ If sudoedit is throwing segfaults for other reasons this will pick those up too.
#### Reference
+
* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit
+
#### Test Dataset
@@ -12552,9 +13290,11 @@ unknown
#### Reference
+
* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit
+
#### Test Dataset
@@ -12621,9 +13361,11 @@ None thus far found
#### Reference
+
* https://www.lares.com/blog/from-lares-labs-defensive-guidance-for-zerologon-cve-2020-1472/
+
#### Test Dataset
@@ -12858,9 +13600,11 @@ None identified.
#### Reference
+
* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
+
#### Test Dataset
@@ -13080,9 +13824,11 @@ The query is structured in a way that `action` (read, create) is not defined. Re
#### Reference
+
* https://raw.githubusercontent.com/Azure/Azure-Sentinel/master/Sample%20Data/Feeds/MSTICIoCs-ExchangeServerVulnerabilitiesDisclosedMarch2021.csv
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/windows-sysmon_proxylogon.log
@@ -13142,6 +13888,7 @@ unknown
#### Reference
+
* https://www.ptsecurity.com/ww-en/about/news/f5-fixes-critical-vulnerability-discovered-by-positive-technologies-in-big-ip-application-delivery-controller/
* https://support.f5.com/csp/article/K52145254
@@ -13149,6 +13896,7 @@ unknown
* https://blog.cloudflare.com/cve-2020-5902-helping-to-protect-against-the-f5-tmui-rce-vulnerability/
+
#### Test Dataset
@@ -13307,6 +14055,7 @@ Although unlikely a renamed instance of hh.exe will be used legitimately, filter
#### Reference
+
* https://attack.mitre.org/techniques/T1218/001/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md
@@ -13314,6 +14063,7 @@ Although unlikely a renamed instance of hh.exe will be used legitimately, filter
* https://lolbas-project.github.io/lolbas/Binaries/Hh/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log
@@ -13391,6 +14141,7 @@ Although unlikely, some legitimate applications (ex. web browsers) may spawn a c
#### Reference
+
* https://attack.mitre.org/techniques/T1218/001/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md
@@ -13402,6 +14153,7 @@ Although unlikely, some legitimate applications (ex. web browsers) may spawn a c
* https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log
@@ -13477,6 +14229,7 @@ Although unlikely, some legitimate applications may retrieve a CHM remotely, fil
#### Reference
+
* https://attack.mitre.org/techniques/T1218/001/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md
@@ -13490,6 +14243,7 @@ Although unlikely, some legitimate applications may retrieve a CHM remotely, fil
* https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log
@@ -13563,6 +14317,7 @@ It is rare to see instances of InfoTech Storage Handlers being used, but it does
#### Reference
+
* https://attack.mitre.org/techniques/T1218/001/
* https://www.kb.cert.org/vuls/id/851869
@@ -13576,6 +14331,7 @@ It is rare to see instances of InfoTech Storage Handlers being used, but it does
* https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log
@@ -13665,6 +14421,7 @@ None currently known
#### Reference
+
* https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2019/pdf/BRKSEC-3200.pdf
* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-ra-guard.html
@@ -13682,6 +14439,7 @@ None currently known
* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ipv6-dest-guard.html
+
#### Test Dataset
@@ -13754,9 +14512,11 @@ Older systems that support kerberos RC4 by default NetApp may generate false pos
#### Reference
+
* Initial ESCU implementation by Jose Hernandez and Patrick Bareiss
+
#### Test Dataset
@@ -13977,6 +14737,7 @@ It is possible legitimate applications may perform this behavior and will need t
#### Reference
+
* https://github.com/redcanaryco/AtomicTestHarnesses
* https://redcanary.com/blog/introducing-atomictestharnesses/
@@ -13984,6 +14745,7 @@ It is possible legitimate applications may perform this behavior and will need t
* https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log
@@ -14060,9 +14822,11 @@ Other tools can import the same DLLs. These tools should be part of a whitelist.
#### Reference
+
* https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log
@@ -14541,91 +15305,6 @@ _version_: 2
---
-### Detect Oulook exe writing a zip file
-This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk.
-
-- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
-- **Datamodel**:
-- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566.001/)
-- **Last Updated**: 2020-07-21
-
-
- details
-
-#### Search
-```
-
-| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe OR Processes.process_name=explorer.exe by _time span=5m Processes.parent_process_id Processes.process_id Processes.dest Processes.process_name Processes.parent_process_name Processes.user
-| `drop_dm_object_name(Processes)`
-| `security_content_ctime(firstTime)`
-| `security_content_ctime(lastTime)`
-| rename process_id as malicious_id
-| rename parent_process_id as outlook_id
-| join malicious_id type=inner[
-| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where (Filesystem.file_path=*zip* OR Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users* OR Filesystem.file_path=*Local\\Temp*) by _time span=5m Filesystem.process_id Filesystem.file_hash Filesystem.dest
-| `drop_dm_object_name(Filesystem)`
-| `security_content_ctime(firstTime)`
-| `security_content_ctime(lastTime)`
-| rename process_id as malicious_id
-| fields malicious_id outlook_id dest file_path file_name file_hash count file_id]
-| table firstTime lastTime user malicious_id outlook_id process_name parent_process_name file_name file_path
-| where file_name != ""
-| `detect_oulook_exe_writing_a__zip_file_filter`
-```
-#### Associated Analytic Story
-
-* Phishing Payloads
-
-
-#### How To Implement
-You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon.
-
-#### Required field
-
-* _time
-
-* Processes.process_name
-
-* Processes.parent_process_id
-
-* Processes.process_id
-
-* Processes.dest
-
-* Processes.parent_process_name
-
-* Processes.user
-
-
-
-#### ATT&CK
-
-| ID | Technique | Tactic |
-| ----------- | ----------- |--------------|
-| T1566.001 | Spearphishing Attachment | Initial Access |
-
-
-#### Kill Chain Phase
-
-* Installation
-
-* Actions on Objectives
-
-
-#### Known False Positives
-It is not uncommon for outlook to write legitimate zip files to the disk.
-
-#### Reference
-
-
-#### Test Dataset
-
-
-_version_: 3
-
-
----
-
### Detect Outbound SMB Traffic
This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor.
@@ -14701,6 +15380,91 @@ It is likely that the outbound Server Message Block (SMB) traffic is legitimate,
#### Test Dataset
+_version_: 3
+
+
+---
+
+### Detect Outlook exe writing a zip file
+This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
+- **Datamodel**:
+- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566.001/)
+- **Last Updated**: 2020-07-21
+
+
+ details
+
+#### Search
+```
+
+| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe OR Processes.process_name=explorer.exe by _time span=5m Processes.parent_process_id Processes.process_id Processes.dest Processes.process_name Processes.parent_process_name Processes.user
+| `drop_dm_object_name(Processes)`
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| rename process_id as malicious_id
+| rename parent_process_id as outlook_id
+| join malicious_id type=inner[
+| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where (Filesystem.file_path=*zip* OR Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users* OR Filesystem.file_path=*Local\\Temp*) by _time span=5m Filesystem.process_id Filesystem.file_hash Filesystem.dest
+| `drop_dm_object_name(Filesystem)`
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| rename process_id as malicious_id
+| fields malicious_id outlook_id dest file_path file_name file_hash count file_id]
+| table firstTime lastTime user malicious_id outlook_id process_name parent_process_name file_name file_path
+| where file_name != ""
+| `detect_outlook_exe_writing_a_zip_file_filter`
+```
+#### Associated Analytic Story
+
+* Spearphishing Attachments
+
+
+#### How To Implement
+You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response product, such as Carbon Black, or endpoint data sources, such as Sysmon.
+
+#### Required field
+
+* _time
+
+* Processes.process_name
+
+* Processes.parent_process_id
+
+* Processes.process_id
+
+* Processes.dest
+
+* Processes.parent_process_name
+
+* Processes.user
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1566.001 | Spearphishing Attachment | Initial Access |
+
+
+#### Kill Chain Phase
+
+* Installation
+
+* Actions on Objectives
+
+
+#### Known False Positives
+It is not uncommon for outlook to write legitimate zip files to the disk.
+
+#### Reference
+
+
+#### Test Dataset
+
+
_version_: 3
@@ -14773,9 +15537,11 @@ Legitimate logon activity by authorized NTLM systems may be detected by this sea
#### Reference
+
* Initial ESCU implementation by Bhavin Patel and Patrick Bareiss
+
#### Test Dataset
@@ -14852,9 +15618,11 @@ unknown
#### Reference
+
* https://medium.com/@SumitVerma101/windows-privilege-escalation-part-1-unquoted-service-path-c7a011a8d8ae
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.009/atomic_red_team/windows-sysmon.log
@@ -15326,6 +16094,7 @@ Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a fa
#### Reference
+
* https://attack.mitre.org/techniques/T1218/009/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md
@@ -15335,6 +16104,7 @@ Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a fa
* https://lolbas-project.github.io/lolbas/Binaries/Regasm/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log
@@ -15412,6 +16182,7 @@ Although unlikely, limited instances of regasm.exe with a network connection may
#### Reference
+
* https://attack.mitre.org/techniques/T1218/009/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md
@@ -15419,6 +16190,7 @@ Although unlikely, limited instances of regasm.exe with a network connection may
* https://lolbas-project.github.io/lolbas/Binaries/Regasm/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log
@@ -15501,6 +16273,7 @@ Although unlikely, limited instances of regasm.exe or may cause a false positive
#### Reference
+
* https://attack.mitre.org/techniques/T1218/009/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md
@@ -15508,6 +16281,7 @@ Although unlikely, limited instances of regasm.exe or may cause a false positive
* https://lolbas-project.github.io/lolbas/Binaries/Regasm/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log
@@ -15585,6 +16359,7 @@ Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a fa
#### Reference
+
* https://attack.mitre.org/techniques/T1218/009/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md
@@ -15592,6 +16367,7 @@ Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a fa
* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log
@@ -15667,6 +16443,7 @@ Although unlikely, limited instances of regsvcs.exe may cause a false positive.
#### Reference
+
* https://attack.mitre.org/techniques/T1218/009/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md
@@ -15674,6 +16451,7 @@ Although unlikely, limited instances of regsvcs.exe may cause a false positive.
* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log
@@ -15758,6 +16536,7 @@ Although unlikely, limited instances of regsvcs.exe may cause a false positive.
#### Reference
+
* https://attack.mitre.org/techniques/T1218/009/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md
@@ -15765,6 +16544,7 @@ Although unlikely, limited instances of regsvcs.exe may cause a false positive.
* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log
@@ -15843,6 +16623,7 @@ Limited false positives related to third party software registering .DLL's.
#### Reference
+
* https://attack.mitre.org/techniques/T1218/010/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md
@@ -15852,6 +16633,7 @@ Limited false positives related to third party software registering .DLL's.
* https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log
@@ -15996,6 +16778,7 @@ Although unlikely, some legitimate applications may use advpack.dll or ieadvpack
#### Reference
+
* https://attack.mitre.org/techniques/T1218/011/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
@@ -16007,6 +16790,7 @@ Although unlikely, some legitimate applications may use advpack.dll or ieadvpack
* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
@@ -16076,6 +16860,7 @@ Although unlikely, some legitimate applications may use setupapi triggering a fa
#### Reference
+
* https://attack.mitre.org/techniques/T1218/011/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
@@ -16087,6 +16872,7 @@ Although unlikely, some legitimate applications may use setupapi triggering a fa
* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
@@ -16156,6 +16942,7 @@ Although unlikely, some legitimate applications may use syssetup.dll, triggering
#### Reference
+
* https://attack.mitre.org/techniques/T1218/011/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
@@ -16167,6 +16954,7 @@ Although unlikely, some legitimate applications may use syssetup.dll, triggering
* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
@@ -16242,6 +17030,7 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg
#### Reference
+
* https://github.com/redcanaryco/AtomicTestHarnesses
* https://redcanary.com/blog/introducing-atomictestharnesses/
@@ -16249,6 +17038,7 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg
* https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log
@@ -16400,6 +17190,7 @@ Unknown
#### Reference
+
* https://www.mnemonic.no/blog/introducing-snicat/
* https://github.com/mnemonic-no/SNIcat
@@ -16407,6 +17198,7 @@ Unknown
* https://attack.mitre.org/techniques/T1041/
+
#### Test Dataset
@@ -17344,9 +18136,11 @@ unknown
#### Reference
+
* https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/
+
#### Test Dataset
@@ -17418,9 +18212,11 @@ unknown
#### Reference
+
* https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/
+
#### Test Dataset
@@ -17481,6 +18277,7 @@ unknown
#### Reference
+
* https://www.secura.com/blog/zero-logon
* https://github.com/SecuraBV/CVE-2020-1472
@@ -17488,6 +18285,7 @@ unknown
* https://portal.msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2020-1472
+
#### Test Dataset
@@ -17783,6 +18581,7 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg
#### Reference
+
* https://github.com/redcanaryco/AtomicTestHarnesses
* https://redcanary.com/blog/introducing-atomictestharnesses/
@@ -17790,6 +18589,7 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg
* https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log
@@ -17867,11 +18667,13 @@ Although unlikely, some legitimate applications may use a moved copy of mshta.ex
#### Reference
+
* https://github.com/redcanaryco/AtomicTestHarnesses
* https://redcanary.com/blog/introducing-atomictestharnesses/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log
@@ -18391,9 +19193,11 @@ admin may disable this application for non technical user.
#### Reference
+
* https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log
@@ -18470,9 +19274,11 @@ unknown
#### Reference
+
* https://www.sophos.com/en-us/threat-center/threat-analyses/viruses-and-spyware/W32~Tiotua-P/detailed-analysis.aspx
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log
@@ -18548,9 +19354,11 @@ admin or user may choose to disable this windows features.
#### Reference
+
* https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log
@@ -18626,9 +19434,11 @@ admin or user may choose to disable this windows features.
#### Reference
+
* https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log
@@ -18704,9 +19514,11 @@ admin may disable this application for non technical user.
#### Reference
+
* https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log
@@ -18782,9 +19594,11 @@ admin may disable this application for non technical user.
#### Reference
+
* https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log
@@ -18866,9 +19680,11 @@ admin may disable firewall during testing or fixing network problem.
#### Reference
+
* https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.htm
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log
@@ -18944,9 +19760,11 @@ admin may disable this application for non technical user.
#### Reference
+
* https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log
@@ -19022,11 +19840,13 @@ admin may disable this application for non technical user.
#### Reference
+
* https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry
* https://blog.malwarebytes.com/detections/pum-optional-norun/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log
@@ -19176,9 +19996,11 @@ in some cases admin can disable systemrestore on a machine.
#### Reference
+
* https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log
@@ -19254,11 +20076,13 @@ admin may disable this application for non technical user.
#### Reference
+
* https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry
* https://blog.talosintelligence.com/2020/05/threat-roundup-0424-0501.html
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log
@@ -19336,11 +20160,13 @@ None identified.
#### Reference
+
* https://modexp.wordpress.com/2019/08/30/minidumpwritedump-via-com-services-dll/
* https://twitter.com/SBousseaden/status/1167417096374050817
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
@@ -19413,6 +20239,7 @@ None identified.
#### Reference
+
* https://attack.mitre.org/techniques/T1003/001/
* https://docs.microsoft.com/en-us/sysinternals/downloads/procdump
@@ -19420,6 +20247,7 @@ None identified.
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
@@ -19496,6 +20324,7 @@ None identified.
#### Reference
+
* https://attack.mitre.org/techniques/T1003/001/
* https://docs.microsoft.com/en-us/sysinternals/downloads/procdump
@@ -19503,6 +20332,7 @@ None identified.
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log
@@ -20140,6 +20970,7 @@ Some false positives may be present and will need to be filtered.
#### Reference
+
* https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1548.002/T1548.002.md
@@ -20149,11 +20980,172 @@ Some false positives may be present and will need to be filtered.
* https://enigma0x3.net/2016/08/15/fileless-uac-bypass-using-eventvwr-exe-and-registry-hijacking/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log
+_version_: 1
+
+
+---
+
+### Excel Spawning PowerShell
+The following detection identifies Microsoft Excel spawning PowerShell. Typically, this is not common behavior and not default with Excel.exe. Excel.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). PowerShell spawning from Excel.exe is common for a spearphishing attachment and is actively used. Albeit, the command executed will most likely be encoded and captured via another detection. During triage, review parallel processes and identify any files that may have been written.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
+- **Datamodel**: Endpoint
+- **ATT&CK**: [T1003.002](https://attack.mitre.org/techniques/T1003.002/)
+- **Last Updated**: 2021-04-12
+
+
+ details
+
+#### Search
+```
+
+| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="excel.exe" Processes.process_name IN ("powershell.exe", "pwsh.exe") by Processes.parent_process Processes.process_name Processes.user Processes.dest
+| `drop_dm_object_name("Processes")`
+| `security_content_ctime(firstTime)`
+|`security_content_ctime(lastTime)`
+| `excel_spawning_powershell_filter`
+```
+#### Associated Analytic Story
+
+* Spearphishing Attachments
+
+
+#### How To Implement
+To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
+
+#### Required field
+
+* _time
+
+* process_name
+
+* process_id
+
+* parent_process_name
+
+* dest
+
+* user
+
+* parent_process_id
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1003.002 | Security Account Manager | Credential Access |
+
+
+#### Kill Chain Phase
+
+* Exploitation
+
+
+#### Known False Positives
+False positives should be limited, but if any are present, filter as needed.
+
+#### Reference
+
+
+* https://redcanary.com/threat-detection-report/techniques/powershell/
+
+* https://attack.mitre.org/techniques/T1566/001/
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log
+
+
+_version_: 1
+
+
+---
+
+### Excel Spawning Windows Script Host
+The following detection identifies Microsoft Excel spawning Windows Script Host - `cscript.exe` or `wscript.exe`. Typically, this is not common behavior and not default with Excel.exe. Excel.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). `cscript.exe` or `wscript.exe` default location is `c:\windows\system32\` or c:windows\syswow64`. `cscript.exe` or `wscript.exe` spawning from Excel.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line executed will most likely be obfuscated and captured via another detection. During triage, review parallel processes and identify any files that may have been written. Review the reputation of the remote destination and block accordingly.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
+- **Datamodel**: Endpoint
+- **ATT&CK**: [T1003.002](https://attack.mitre.org/techniques/T1003.002/)
+- **Last Updated**: 2021-04-12
+
+
+ details
+
+#### Search
+```
+
+| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="excel.exe" Processes.process_name IN ("cscript.exe", "wscript.exe") by Processes.parent_process Processes.process_name Processes.user Processes.dest
+| `drop_dm_object_name("Processes")`
+| `security_content_ctime(firstTime)`
+|`security_content_ctime(lastTime)`
+| `excel_spawning_windows_script_host_filter`
+```
+#### Associated Analytic Story
+
+* Spearphishing Attachments
+
+
+#### How To Implement
+To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
+
+#### Required field
+
+* _time
+
+* process_name
+
+* process_id
+
+* parent_process_name
+
+* dest
+
+* user
+
+* parent_process_id
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1003.002 | Security Account Manager | Credential Access |
+
+
+#### Kill Chain Phase
+
+* Exploitation
+
+
+#### Known False Positives
+False positives should be limited, but if any are present, filter as needed. In some instances, `cscript.exe` is used for legitimate business practices.
+
+#### Reference
+
+
+* https://app.any.run/tasks/8ecfbc29-03d0-421c-a5bf-3905d29192a2/
+
+* https://attack.mitre.org/techniques/T1566/001/
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log
+
+
_version_: 1
@@ -20889,6 +21881,7 @@ Limited to no false positives are expected.
#### Reference
+
* https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1548.002/T1548.002.md
@@ -20898,6 +21891,7 @@ Limited to no false positives are expected.
* https://attack.mitre.org/techniques/T1548/002
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log
@@ -20968,6 +21962,7 @@ Accounts with high risk roles should be reduced to the minimum number needed, ho
#### Reference
+
* https://github.com/dxa4481/gcploit
* https://www.youtube.com/watch?v=Ml09R38jpok
@@ -20975,6 +21970,7 @@ Accounts with high risk roles should be reduced to the minimum number needed, ho
* https://cloud.google.com/iam/docs/understanding-roles
+
#### Test Dataset
@@ -21047,11 +22043,13 @@ Payload.request.function.timeout value can possibly be match with other function
#### Reference
+
* https://github.com/dxa4481/gcploit
* https://www.youtube.com/watch?v=Ml09R38jpok
+
#### Test Dataset
@@ -21120,6 +22118,7 @@ High risk permissions are part of any GCP environment, however it is important t
#### Reference
+
* https://github.com/dxa4481/gcploit
* https://www.youtube.com/watch?v=Ml09R38jpok
@@ -21127,6 +22126,7 @@ High risk permissions are part of any GCP environment, however it is important t
* https://cloud.google.com/iam/docs/permissions-reference
+
#### Test Dataset
@@ -21322,6 +22322,92 @@ Not all unauthenticated requests are malicious, but frequency, User Agent and so
#### Test Dataset
+_version_: 1
+
+
+---
+
+### GPUpdate with no Command Line Arguments with Network
+The following analytic identifies gpupdate.exe with no command line arguments and with a network connection. It is unusual for gpupdate.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. gpupdate.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
+- **Datamodel**: Endpoint
+- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/)
+- **Last Updated**: 2021-04-19
+
+
+ details
+
+#### Search
+```
+
+| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=gpupdate.exe by _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name
+| `drop_dm_object_name(Processes)`
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| regex process="(gpupdate\.exe.{0,4}$)"
+| join process_id [
+| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port
+| `drop_dm_object_name(Ports)`
+| rename dest as connection_to_CNC]
+| table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port
+| `gpupdate_with_no_command_line_arguments_with_network_filter`
+```
+#### Associated Analytic Story
+
+* Cobalt Strike
+
+
+#### How To Implement
+To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
+
+#### Required field
+
+* _time
+
+* EventID
+
+* process_name
+
+* process_id
+
+* parent_process_name
+
+* dest_port
+
+* process_path
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1055 | Process Injection | Defense Evasion, Privilege Escalation |
+
+
+#### Kill Chain Phase
+
+* Exploitation
+
+
+#### Known False Positives
+Limited false positives may be present in small environments. Tuning may be required based on parent process.
+
+#### Reference
+
+
+* https://raw.githubusercontent.com/xx0hcd/Malleable-C2-Profiles/0ef8cf4556e26f6d4190c56ba697c2159faa5822/crimeware/trick_ryuk.profile
+
+* https://blog.cobaltstrike.com/2021/02/09/learn-pipe-fitting-for-all-of-your-offense-projects/
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
+
+
_version_: 1
@@ -21464,11 +22550,13 @@ user may delete bunch of pictures or files in a folder.
#### Reference
+
* https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html
* https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log
@@ -21617,11 +22705,13 @@ admin or user tool that can terminate multiple process.
#### Reference
+
* https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html
* https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log
@@ -21824,9 +22914,11 @@ None identified.
#### Reference
+
* https://github.com/PowerShellMafia/PowerSploit
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021/illegal_access_to_content/logAllPowerSploitModulesWithOldNames.log
@@ -21896,9 +22988,11 @@ None identified.
#### Reference
+
* https://github.com/PowerShellMafia/PowerSploit
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1585/illegal_account_creation/logAllPowerSploitModulesWithOldNames.log
@@ -21968,9 +23062,11 @@ None identified.
#### Reference
+
* https://github.com/gentilkiwi/mimikatz
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/illegal_log_deletion/logAllMimikatzModules.log
@@ -22041,9 +23137,11 @@ None identified.
#### Reference
+
* https://github.com/MichaelGrafnetter/DSInternals
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/logAllDSInternalsModules.log
@@ -22115,9 +23213,11 @@ None identified.
#### Reference
+
* https://github.com/MichaelGrafnetter/DSInternals
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484/logAllDSInternalsModules.log
@@ -22190,9 +23290,11 @@ None identified.
#### Reference
+
* https://github.com/PowerShellMafia/PowerSploit
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484/logAllPowerSploitModulesWithOldNames.log
@@ -22266,9 +23368,11 @@ None identified.
#### Reference
+
* https://github.com/PowerShellMafia/PowerSploit
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/logAllPowerSploitModulesWithOldNames.log
@@ -22339,9 +23443,11 @@ None identified.
#### Reference
+
* https://github.com/gentilkiwi/mimikatz
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/logAllMimikatzModules.log
@@ -22413,9 +23519,11 @@ None identified.
#### Reference
+
* https://github.com/gentilkiwi/mimikatz
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logAllMimikatzModules.log
@@ -22490,9 +23598,11 @@ None identified.
#### Reference
+
* https://github.com/PowerShellMafia/PowerSploit
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logAllPowerSploitModulesWithOldNames.log
@@ -22565,11 +23675,13 @@ Older systems that support kerberos RC4 by default NetApp may generate false pos
#### Reference
+
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1208/T1208.md
* https://www.trimarcsecurity.com/post/trimarcresearch-detecting-kerberoasting-activity
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/atomic_red_team/windows-security.log
@@ -24180,6 +25292,7 @@ Creating a hidden powershell service is rare and could key off of those instance
#### Reference
+
* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/dosfuscation-report.pdf
* http://az4n6.blogspot.com/2017/
@@ -24187,6 +25300,7 @@ Creating a hidden powershell service is rare and could key off of those instance
* https://www.danielbohannon.com/blog-1/2017/3/12/powershell-execution-argument-obfuscation-how-it-can-make-detection-easier
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/atomic_red_team/windows-system.log
@@ -24514,9 +25628,11 @@ Some administrative tasks may involve multiple use of LOLBAS applications in a s
#### Reference
+
* https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries
+
#### Test Dataset
@@ -24665,6 +25781,7 @@ Administrators may use nltest for troubleshooting purposes, otherwise, rarely us
#### Reference
+
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1482/T1482.md
* https://malware.news/t/lets-learn-trickbot-implements-network-collector-module-leveraging-cmd-wmi-ldap/19104
@@ -24680,6 +25797,7 @@ Administrators may use nltest for troubleshooting purposes, otherwise, rarely us
* https://thedfirreport.com/2020/10/08/ryuks-return/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/atomic_red_team/windows-sysmon.log
@@ -24813,6 +25931,7 @@ Limited false positives may be present. Filter as needed based on initial analys
#### Reference
+
* https://github.com/samratashok/nishang/blob/master/Shells/Invoke-PowerShellTcpOneLine.ps1
* https://www.volexity.com/blog/2021/03/02/active-exploitation-of-microsoft-exchange-zero-day-vulnerabilities/
@@ -24822,6 +25941,7 @@ Limited false positives may be present. Filter as needed based on initial analys
* https://blog.rapid7.com/2021/03/03/rapid7s-insightidr-enables-detection-and-response-to-microsoft-exchange-0-day/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log
@@ -24964,6 +26084,7 @@ Highly possible Server Administrators will troubleshoot with ntdsutil.exe, gener
#### Reference
+
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.003/T1003.003.md#atomic-test-3---dump-active-directory-database-with-ntdsutil
* https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc753343(v=ws.11)
@@ -24973,6 +26094,7 @@ Highly possible Server Administrators will troubleshoot with ntdsutil.exe, gener
* https://strontic.github.io/xcyclopedia/library/vss_ps.dll-97B15BDAE9777F454C9A6BA25E938DB3.html
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log
@@ -25049,11 +26171,13 @@ The creation of a new Federation is not necessarily malicious, however this even
#### Reference
+
* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf
* https://us-cert.cisa.gov/ncas/alerts/aa21-008a
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federation/o365_new_federation.json
@@ -25130,6 +26254,7 @@ The creation of a new Federation is not necessarily malicious, however these eve
#### Reference
+
* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf
* https://us-cert.cisa.gov/ncas/alerts/aa21-008a
@@ -25139,6 +26264,7 @@ The creation of a new Federation is not necessarily malicious, however these eve
* https://www.sygnia.co/golden-saml-advisory
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_add_service_principal/o365_add_service_principal.json
@@ -25224,11 +26350,13 @@ Unless it is a special case, it is uncommon to continually update Trusted IPs to
#### Reference
+
* https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf
* https://attack.mitre.org/techniques/T1562/007/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/o365_bypass_mfa_via_trusted_ip/o365_bypass_mfa_via_trusted_ip.json
@@ -25303,9 +26431,11 @@ Unless it is a special case, it is uncommon to disable MFA or Strong Authenticat
#### Reference
+
* https://attack.mitre.org/techniques/T1556/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_disable_mfa/o365_disable_mfa.json
@@ -25379,9 +26509,11 @@ The threshold for alert is above 10 attempts and this should reduce the number o
#### Reference
+
* https://attack.mitre.org/techniques/T1110/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/o365_brute_force_login/o365_brute_force_login.json
@@ -25455,9 +26587,11 @@ Logon errors may not be malicious in nature however it may indicate attempts to
#### Reference
+
* https://stealthbits.com/blog/bypassing-mfa-with-pass-the-cookie/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_sso_logon_errors/o365_sso_logon_errors.json
@@ -25536,6 +26670,7 @@ The creation of a new Federated domain is not necessarily malicious, however the
#### Reference
+
* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf
* https://us-cert.cisa.gov/ncas/alerts/aa21-008a
@@ -25547,6 +26682,7 @@ The creation of a new Federated domain is not necessarily malicious, however the
* https://o365blog.com/post/aadbackdoor/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federated_domain/o365_new_federated_domain.json
@@ -25619,9 +26755,11 @@ PST export can be done for legitimate purposes but due to the sensitive nature o
#### Reference
+
* https://attack.mitre.org/techniques/T1114/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_export_pst_file/o365_export_pst_file.json
@@ -25833,6 +26971,415 @@ unknown
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule/o365_email_forwarding_rule.json
+_version_: 1
+
+
+---
+
+### Office Application Spawn rundll32 process
+this detection was designed to identifies suspicious spawned process of known MS office application due to macro or malicious code. this technique can be seen in so many malware like trickbot that used MS office as its weapon or attack vector to initially infect the machines.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
+- **Datamodel**: Endpoint
+- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566.001/)
+- **Last Updated**: 2021-04-13
+
+
+ details
+
+#### Search
+```
+
+| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "winword.exe" OR Processes.parent_process_name = "excel.exe" OR Processes.parent_process_name = "powerpnt.exe" OR Processes.parent_process_name = "wordpad.exe") Processes.process_name=rundll32.exe by Processes.parent_process Processes.process_name Processes.process_id Processes.process_guid Processes.user Processes.dest
+| `drop_dm_object_name("Processes")`
+| `security_content_ctime(firstTime)`
+|`security_content_ctime(lastTime)`
+| `office_application_spawn_rundll32_process_filter`
+```
+#### Associated Analytic Story
+
+* Spearphishing Attachments
+
+
+#### How To Implement
+To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+
+#### Required field
+
+* Processes.process
+
+* Processes.parent_process_name
+
+* _time
+
+* Processes.process_name
+
+* Processes.dest
+
+* Processes.user
+
+* Processes.process_id
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1566.001 | Spearphishing Attachment | Initial Access |
+
+
+#### Kill Chain Phase
+
+* Exploitation
+
+
+#### Known False Positives
+unknown
+
+#### Reference
+
+
+* https://any.run/malware-trends/trickbot
+
+* https://any.run/report/47561b4e949041eff0a0f4693c59c81726591779fe21183ae9185b5eb6a69847/aba3722a-b373-4dae-8273-8730fb40cdbe
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log
+
+
+_version_: 1
+
+
+---
+
+### Office Document Creating Schedule Task
+this search detects a potential malicious office document that create schedule task entry through macro VBA api or through loading taskschd.dll. This technique was seen in so many malicious macro malware that create persistence , beaconing using task schedule malware entry The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it's possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.'
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
+- **Datamodel**: Endpoint
+- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566.001/)
+- **Last Updated**: 2021-04-14
+
+
+ details
+
+#### Search
+```
+`sysmon` EventCode=7 process_name IN ("WINWORD.EXE", "EXCEL.EXE", "POWERPNT.EXE", "wordpad.exe") ImageLoaded = "*\\taskschd.dll"
+| stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) as AllImageLoaded count by Computer EventCode Image process_name ProcessId ProcessGuid
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `office_document_creating_schedule_task_filter`
+```
+#### Associated Analytic Story
+
+* Spearphishing Attachments
+
+
+#### How To Implement
+To successfully implement this search, you need to be ingesting logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Also be sure to include those monitored dll to your own sysmon config.
+
+#### Required field
+
+* ImageLoaded
+
+* AllImageLoaded
+
+* Computer
+
+* EventCode
+
+* Image
+
+* process_name
+
+* ProcessId
+
+* ProcessGuid
+
+* _time
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1566.001 | Spearphishing Attachment | Initial Access |
+
+
+#### Kill Chain Phase
+
+* Exploitation
+
+
+#### Known False Positives
+unknown
+
+#### Reference
+
+
+* https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/
+
+* https://redcanary.com/threat-detection-report/techniques/scheduled-task-job/
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log
+
+
+_version_: 1
+
+
+---
+
+### Office Document Executing Macro Code
+this detection was designed to identifies suspicious office documents that using macro code. Macro code is known to be one of the prevalent weaponization or attack vector of threat actor. This malicious macro code is embed to a office document as an attachment that may execute malicious payload, download malware payload or other malware component. It is really good practice to disable macro by default to avoid automatically execute macro code while opening or closing a office document files.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
+- **Datamodel**: Endpoint
+- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566.001/)
+- **Last Updated**: 2021-04-14
+
+
+ details
+
+#### Search
+```
+`sysmon` EventCode=7 process_name IN ("WINWORD.EXE", "EXCEL.EXE", "POWERPNT.EXE", "wordpad.exe") ImageLoaded IN ("*\\VBE7INTL.DLL","*\\VBE7.DLL", "*\\VBEUI.DLL")
+| stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) as AllImageLoaded count by Computer EventCode Image process_name ProcessId ProcessGuid
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `office_document_executing_macro_code_filter`
+```
+#### Associated Analytic Story
+
+* Spearphishing Attachments
+
+
+#### How To Implement
+To successfully implement this search, you need to be ingesting logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Also be sure to include those monitored dll to your own sysmon config.
+
+#### Required field
+
+* ImageLoaded
+
+* AllImageLoaded
+
+* Computer
+
+* EventCode
+
+* Image
+
+* process_name
+
+* ProcessId
+
+* ProcessGuid
+
+* _time
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1566.001 | Spearphishing Attachment | Initial Access |
+
+
+#### Kill Chain Phase
+
+* Exploitation
+
+
+#### Known False Positives
+Normal Office Document macro use for automation
+
+#### Reference
+
+
+* https://www.joesandbox.com/analysis/386500/0/html
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log
+
+
+_version_: 1
+
+
+---
+
+### Office Document Spawned Child Process To Download
+this search is to detect potential malicious office document executing lolbin child process to download payload or other malware. Since most of the attacker abused the capability of office document to execute living on land application to blend it to the normal noise in the infected machine to cover its track.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
+- **Datamodel**: Endpoint
+- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566.001/)
+- **Last Updated**: 2021-04-16
+
+
+ details
+
+#### Search
+```
+`sysmon` EventCode=1 parent_process_name IN ("powerpnt.exe", "winword.exe", "excel.exe", "visio.exe", "wordpad.exe") process_name = "*.exe" cmdline IN ("*http:*","*https:*") NOT(OriginalFileName IN("*\\firefox.exe", "*\\chrome.exe","*\\iexplore.exe","*\\msedge.exe"))
+| stats min(_time) as firstTime max(_time) as lastTime count by parent_process_name process_name parent_process cmdline process_id OriginalFileName ProcessGuid Computer EventCode
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `office_document_spawned_child_process_to_download_filter`
+```
+#### Associated Analytic Story
+
+* Spearphishing Attachments
+
+
+#### How To Implement
+To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances office application and browser may be used.
+
+#### Required field
+
+* _time
+
+* parent_process_name
+
+* process_name
+
+* parent_process
+
+* cmdline
+
+* process_id
+
+* OriginalFileName
+
+* ProcessGuid
+
+* Computer
+
+* EventCode
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1566.001 | Spearphishing Attachment | Initial Access |
+
+
+#### Kill Chain Phase
+
+* Exploitation
+
+
+#### Known False Positives
+default browser not in the filter list
+
+#### Reference
+
+
+* https://app.any.run/tasks/92d7ef61-bfd7-4c92-bc15-322172b4ebec/#
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets2/windows-sysmon.log
+
+
+_version_: 1
+
+
+---
+
+### Office Product Spawning Rundll32 with no DLL
+The following detection identifies the latest behavior utilized by IcedID malware family. This detection identifies any Windows Office Product spawning `rundll32.exe` without a `.dll` file extension. In malicious instances, the command-line of `rundll32.exe` will look like `rundll32 ..\oepddl.igk2,DllRegisterServer`. In addition, Threat Research has released a detection identifying the use of `DllRegisterServer` on the command-line of `rundll32.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze the `DLL` that was dropped to disk. The Office Product will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
+- **Datamodel**: Endpoint
+- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566.001/)
+- **Last Updated**: 2021-04-22
+
+
+ details
+
+#### Search
+```
+
+| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=rundll32.exe (Processes.process!=*.dll*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+| `drop_dm_object_name(Processes)`
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `office_product_spawning_rundll32_with_no_dll_filter`
+```
+#### Associated Analytic Story
+
+* Spearphishing Attachments
+
+
+#### How To Implement
+To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
+
+#### Required field
+
+* _time
+
+* process_name
+
+* process_id
+
+* parent_process_name
+
+* dest
+
+* user
+
+* parent_process_id
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1566.001 | Spearphishing Attachment | Initial Access |
+
+
+#### Kill Chain Phase
+
+* Exploitation
+
+
+#### Known False Positives
+False positives should be limited, but if any are present, filter as needed.
+
+#### Reference
+
+
+* https://www.joesandbox.com/analysis/395471/0/html
+
+* https://app.any.run/tasks/cef4b8ba-023c-4b3b-b2ef-6486a44f6ed9/
+
+* https://any.run/malware-trends/icedid
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_icedid.log
+
+
_version_: 1
@@ -26335,11 +27882,13 @@ Limited false positives. It is possible administrators will utilize Start-BitsTr
#### Reference
+
* https://isc.sans.edu/diary/Investigating+Microsoft+BITS+Activity/23281
* https://docs.microsoft.com/en-us/windows/win32/bits/using-windows-powershell-to-create-bits-transfer-jobs
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log
@@ -26410,9 +27959,11 @@ None identified.
#### Reference
+
* https://github.com/PowerShellMafia/PowerSploit
+
#### Test Dataset
@@ -26450,7 +28001,7 @@ This search looks for a process launching an `*.lnk` file under `C:\User*` or `*
```
#### Associated Analytic Story
-* Phishing Payloads
+* Spearphishing Attachments
#### How To Implement
@@ -26495,11 +28046,13 @@ This detection should yield little or no false positive results. It is uncommon
#### Reference
+
* https://attack.mitre.org/techniques/T1566/001/
* https://www.trendmicro.com/en_us/research/17/e/rising-trend-attackers-using-lnk-files-download-malware.html
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.002/lnk_file_temp_folder/windows-sysmon.log
@@ -26580,11 +28133,13 @@ unknown
#### Reference
+
* https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html
* https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log
@@ -27210,11 +28765,13 @@ unknown
#### Reference
+
* https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html
* https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log
@@ -27366,9 +28923,11 @@ None identified.
#### Reference
+
* https://github.com/PowerShellMafia/PowerSploit
+
#### Test Dataset
@@ -27438,9 +28997,11 @@ None identified.
#### Reference
+
* https://github.com/gentilkiwi/mimikatz
+
#### Test Dataset
@@ -27512,9 +29073,11 @@ None identified.
#### Reference
+
* https://github.com/PowerShellMafia/PowerSploit
+
#### Test Dataset
@@ -27584,9 +29147,11 @@ None identified.
#### Reference
+
* https://github.com/PowerShellMafia/PowerSploit
+
#### Test Dataset
@@ -27654,9 +29219,11 @@ None identified.
#### Reference
+
* https://github.com/gentilkiwi/mimikatz
+
#### Test Dataset
@@ -27731,9 +29298,11 @@ None identified.
#### Reference
+
* https://github.com/PowerShellMafia/PowerSploit
+
#### Test Dataset
@@ -27803,9 +29372,11 @@ None identified.
#### Reference
+
* https://github.com/gentilkiwi/mimikatz
+
#### Test Dataset
@@ -27875,9 +29446,11 @@ None identified.
#### Reference
+
* https://github.com/gentilkiwi/mimikatz
+
#### Test Dataset
@@ -27947,9 +29520,11 @@ None identified.
#### Reference
+
* https://github.com/PowerShellMafia/PowerSploit
+
#### Test Dataset
@@ -28022,9 +29597,11 @@ None identified.
#### Reference
+
* https://github.com/PowerShellMafia/PowerSploit
+
#### Test Dataset
@@ -28094,9 +29671,11 @@ None identified.
#### Reference
+
* https://github.com/PowerShellMafia/PowerSploit
+
#### Test Dataset
@@ -28169,9 +29748,11 @@ None identified.
#### Reference
+
* https://github.com/gentilkiwi/mimikatz
+
#### Test Dataset
@@ -28240,9 +29821,11 @@ None identified.
#### Reference
+
* https://github.com/PowerShellMafia/PowerSploit
+
#### Test Dataset
@@ -28312,9 +29895,11 @@ None identified.
#### Reference
+
* https://github.com/PowerShellMafia/PowerSploit
+
#### Test Dataset
@@ -28384,11 +29969,13 @@ None identified.
#### Reference
+
* https://github.com/gentilkiwi/mimikatz
* https://en.wikipedia.org/wiki/Microsoft_Detours
+
#### Test Dataset
@@ -28683,9 +30270,11 @@ There are many legitimate applications that must execute upon system startup and
#### Reference
+
* https://blog.malwarebytes.com/101/2015/12/an-introduction-to-image-file-execution-options/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.012/atomic_red_team/windows-sysmon.log
@@ -29244,11 +30833,13 @@ network admin can resize the shadowstorage for valid purposes.
#### Reference
+
* https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html
* https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log
@@ -29331,6 +30922,98 @@ _version_: 4
---
+### Rundll32 with no Command Line Arguments with Network
+The following analytic identifies rundll32.exe with no command line arguments and performing a network connection. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
+- **Datamodel**: Endpoint
+- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218.011/)
+- **Last Updated**: 2021-04-19
+
+
+ details
+
+#### Search
+```
+
+| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe by _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name
+| `drop_dm_object_name(Processes)`
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| regex process="(rundll32\.exe.{0,4}$)"
+| join process_id [
+| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port
+| `drop_dm_object_name(Ports)`
+| rename dest as connection_to_CNC]
+| table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port
+| `rundll32_with_no_command_line_arguments_with_network_filter`
+```
+#### Associated Analytic Story
+
+* Suspicious Rundll32 Activity
+
+* Cobalt Strike
+
+
+#### How To Implement
+To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `port` node.
+
+#### Required field
+
+* _time
+
+* EventID
+
+* process_name
+
+* process_id
+
+* parent_process_name
+
+* dest_port
+
+* process_path
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1218.011 | Rundll32 | Defense Evasion |
+
+
+#### Kill Chain Phase
+
+* Exploitation
+
+
+#### Known False Positives
+Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive.
+
+#### Reference
+
+
+* https://attack.mitre.org/techniques/T1218/011/
+
+* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
+
+* https://lolbas-project.github.io/lolbas/Binaries/Rundll32
+
+* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
+
+
+_version_: 1
+
+
+---
+
### Ryuk Test Files Detected
The search looks for files that contain the key word *Ryuk* under any folder in the C drive, which is consistent with Ryuk propagation.
@@ -29468,6 +31151,7 @@ Limited to no known false positives.
#### Reference
+
* https://www.bleepingcomputer.com/news/security/ryuk-ransomware-uses-wake-on-lan-to-encrypt-offline-devices/
* https://www.bleepingcomputer.com/news/security/ryuk-ransomware-now-self-spreads-to-other-windows-lan-devices/
@@ -29475,6 +31159,7 @@ Limited to no known false positives.
* https://www.cert.ssi.gouv.fr/uploads/CERTFR-2021-CTI-006.pdf
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/ryuk/windows-sysmon.log
@@ -30221,6 +31906,164 @@ _version_: 3
---
+### SearchProtocolHost with no Command Line with Network
+The following analytic identifies searchprotocolhost.exe with no command line arguments and with a network connection. It is unusual for searchprotocolhost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. searchprotocolhost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
+- **Datamodel**: Endpoint
+- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/)
+- **Last Updated**: 2021-04-19
+
+
+ details
+
+#### Search
+```
+
+| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=searchprotocolhost.exe by _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name
+| `drop_dm_object_name(Processes)`
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| regex process="(searchprotocolhost\.exe.{0,4}$)"
+| join process_id [
+| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port
+| `drop_dm_object_name(Ports)`
+| rename dest as connection_to_CNC]
+| table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port
+| `searchprotocolhost_with_no_command_line_with_network_filter`
+```
+#### Associated Analytic Story
+
+* Cobalt Strike
+
+
+#### How To Implement
+To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `ports` node.
+
+#### Required field
+
+* _time
+
+* process_name
+
+* process_id
+
+* parent_process_name
+
+* dest_port
+
+* process_path
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1055 | Process Injection | Defense Evasion, Privilege Escalation |
+
+
+#### Kill Chain Phase
+
+* Exploitation
+
+
+#### Known False Positives
+Limited false positives may be present in small environments. Tuning may be required based on parent process.
+
+#### Reference
+
+
+* https://github.com/fireeye/red_team_tool_countermeasures/blob/master/rules/PGF/supplemental/hxioc/SUSPICIOUS%20EXECUTION%20OF%20SEARCHPROTOCOLHOST%20(METHODOLOGY).ioc
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon_searchprotocolhost.log
+
+
+_version_: 1
+
+
+---
+
+### Set Default PowerShell Execution Policy To Unrestricted or Bypass
+Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
+- **Datamodel**: Endpoint
+- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059.001/)
+- **Last Updated**: 2020-11-06
+
+
+ details
+
+#### Search
+```
+
+| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_key_name=ExecutionPolicy (Registry.registry_value_name=Unrestricted OR Registry.registry_value_name=Bypass) by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest
+| `drop_dm_object_name(Registry)`
+| `security_content_ctime(firstTime)`
+|`security_content_ctime(lastTime)`
+| `set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter`
+```
+#### Associated Analytic Story
+
+* Malicious PowerShell
+
+* Credential Dumping
+
+* HAFNIUM Group
+
+
+#### How To Implement
+You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Registry node. You must also be ingesting logs with the fields registry_path, registry_key_name, and registry_value_name from your endpoints.
+
+#### Required field
+
+* _time
+
+* Registry.registry_path
+
+* Registry.registry_key_name
+
+* Registry.registry_value_name
+
+* Registry.dest
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1059.001 | PowerShell | Execution |
+
+
+#### Kill Chain Phase
+
+* Installation
+
+* Actions on Objectives
+
+
+#### Known False Positives
+Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate.
+
+#### Reference
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_execution_policy/windows-sysmon.log
+
+
+_version_: 6
+
+
+---
+
### Setting Credentials via DSInternals modules
This detection identifies illegal setting of credentials via DSInternals modules.
@@ -30288,9 +32131,11 @@ None identified.
#### Reference
+
* https://github.com/MichaelGrafnetter/DSInternals
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/logAllDSInternalsModules.log
@@ -30362,9 +32207,11 @@ None identified.
#### Reference
+
* https://github.com/gentilkiwi/mimikatz
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/logAllMimikatzModules.log
@@ -30436,14 +32283,97 @@ None identified.
#### Reference
+
* https://github.com/PowerShellMafia/PowerSploit
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/logAllPowerSploitModulesWithOldNames.log
+_version_: 1
+
+
+---
+
+### Shedule Task with HTTP Command Arguments
+The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with an arguments "HTTP" string that are unique entry of malware or attack that uses lolbin to download other file or payload to the infected machine.\ The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not.\ schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`.\ The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory.\ Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.'
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
+- **Datamodel**: Endpoint
+- **ATT&CK**: [T1053](https://attack.mitre.org/techniques/T1053/)
+- **Last Updated**: 2021-04-19
+
+
+ details
+
+#### Search
+```
+`wineventlog_security` EventCode=4698
+| xmlkv Message
+| search Arguments IN ("*http*")
+| stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden, Arguments
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `shedule_task_with_http_command_arguments_filter`
+```
+#### Associated Analytic Story
+
+* Windows Persistence Techniques
+
+
+#### How To Implement
+To successfully implement this search, you need to be ingesting logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and filter known instances of Task schedule used in your environment.
+
+#### Required field
+
+* _time
+
+* dest
+
+* Task_Name
+
+* Command
+
+* Author
+
+* Enabled
+
+* Hidden
+
+* Arguments
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1053 | Scheduled Task/Job | Execution, Persistence, Privilege Escalation |
+
+
+#### Kill Chain Phase
+
+* Exploitation
+
+
+#### Known False Positives
+unknown
+
+#### Reference
+
+
+* https://app.any.run/tasks/92d7ef61-bfd7-4c92-bc15-322172b4ebec/
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/tasksched/windows-security.log
+
+
_version_: 1
@@ -30965,9 +32895,11 @@ unknown
#### Reference
+
* https://www.fireeye.com/blog/threat-research/2020/12/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor.html
+
#### Test Dataset
@@ -31036,11 +32968,13 @@ There might be false positives associted with this detection since items like ar
#### Reference
+
* https://www.splunk.com/en_us/blog/security/detecting-supernova-malware-solarwinds-continued.html
* https://www.guidepointsecurity.com/supernova-solarwinds-net-webshell-analysis/
+
#### Test Dataset
@@ -31182,11 +33116,13 @@ Unknown. Filter as needed.
#### Reference
+
* https://redcanary.com/blog/clipping-silver-sparrows-wings/
* https://marcosantadev.com/manage-plist-files-plistbuddy/
+
#### Test Dataset
@@ -31265,11 +33201,13 @@ Limited false positives may be present in small environments. Tuning may be requ
#### Reference
+
* https://raw.githubusercontent.com/threatexpress/malleable-c2/c3385e481159a759f79b8acfe11acf240893b830/jquery-c2.4.2.profile
* https://blog.cobaltstrike.com/2021/02/09/learn-pipe-fitting-for-all-of-your-offense-projects/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
@@ -31537,11 +33475,13 @@ Limited false positives may be present in small environments. Tuning may be requ
#### Reference
+
* https://raw.githubusercontent.com/xx0hcd/Malleable-C2-Profiles/0ef8cf4556e26f6d4190c56ba697c2159faa5822/crimeware/trick_ryuk.profile
* https://blog.cobaltstrike.com/2021/02/09/learn-pipe-fitting-for-all-of-your-offense-projects/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
@@ -31694,6 +33634,7 @@ Although unlikely, some legitimate applications may use a moved copy of msbuild,
#### Reference
+
* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md
@@ -31701,6 +33642,7 @@ Although unlikely, some legitimate applications may use a moved copy of msbuild,
* https://github.com/infosecn1nja/MaliciousMacroMSBuild/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log
@@ -31774,11 +33716,13 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg
#### Reference
+
* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log
@@ -31861,11 +33805,13 @@ Some legitimate applications may use PlistBuddy to create or modify property lis
#### Reference
+
* https://redcanary.com/blog/clipping-silver-sparrows-wings/
* https://marcosantadev.com/manage-plist-files-plistbuddy/
+
#### Test Dataset
@@ -31930,11 +33876,13 @@ Some legitimate applications may use PlistBuddy to create or modify property lis
#### Reference
+
* https://redcanary.com/blog/clipping-silver-sparrows-wings/
* https://marcosantadev.com/manage-plist-files-plistbuddy/
+
#### Test Dataset
@@ -32020,9 +33968,11 @@ It's possible for system administrators to write scripts that exhibit this behav
#### Reference
+
* https://car.mitre.org/wiki/CAR-2013-03-001
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/atomic_red_team/windows-sysmon.log
@@ -32100,6 +34050,7 @@ Limited false positives with the query restricted to specified paths. Add more w
#### Reference
+
* https://attack.mitre.org/techniques/T1218/010/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md
@@ -32111,6 +34062,7 @@ Limited false positives with the query restricted to specified paths. Add more w
* https://any.run/report/f29a7d2ecd3585e1e4208e44bcc7156ab5388725f1d29d03e7699da0d4598e7c/0826458b-5367-45cf-b841-c95a33a01718
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log
@@ -32189,6 +34141,7 @@ Although unlikely, some legitimate applications may use a moved copy of rundll32
#### Reference
+
* https://attack.mitre.org/techniques/T1218/011/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
@@ -32196,6 +34149,7 @@ Although unlikely, some legitimate applications may use a moved copy of rundll32
* https://lolbas-project.github.io/lolbas/Binaries/Rundll32
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
@@ -32273,6 +34227,7 @@ Although unlikely, some legitimate applications may use Start as a function and
#### Reference
+
* https://attack.mitre.org/techniques/T1218/011/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
@@ -32284,6 +34239,7 @@ Although unlikely, some legitimate applications may use Start as a function and
* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
@@ -32359,6 +34315,7 @@ This is likely to produce false positives and will require some filtering. Tune
#### Reference
+
* https://attack.mitre.org/techniques/T1218/011/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
@@ -32374,6 +34331,7 @@ This is likely to produce false positives and will require some filtering. Tune
* https://msdn.microsoft.com/en-us/library/windows/desktop/ms682162(v=vs.85).aspx
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
@@ -32456,6 +34414,7 @@ Although unlikely, some legitimate applications may use a moved copy of rundll32
#### Reference
+
* https://attack.mitre.org/techniques/T1218/011/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
@@ -32465,6 +34424,7 @@ Although unlikely, some legitimate applications may use a moved copy of rundll32
* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log
@@ -32540,11 +34500,13 @@ Unknown.
#### Reference
+
* https://redcanary.com/blog/clipping-silver-sparrows-wings/
* https://marcosantadev.com/manage-plist-files-plistbuddy/
+
#### Test Dataset
@@ -32626,9 +34588,11 @@ Limited false positives may be present. Filter as needed by parent process or co
#### Reference
+
* https://attack.mitre.org/techniques/T1053/005/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtasks/windows-sysmon.log
@@ -32709,9 +34673,11 @@ Limited false positives may be present in small environments. Tuning may be requ
#### Reference
+
* https://github.com/fireeye/red_team_tool_countermeasures/blob/master/rules/PGF/supplemental/hxioc/SUSPICIOUS%20EXECUTION%20OF%20SEARCHPROTOCOLHOST%20(METHODOLOGY).ioc
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
@@ -32792,11 +34758,13 @@ Although unlikely, some legitimate applications may use a moved copy of microsof
#### Reference
+
* https://lolbas-project.github.io/lolbas/Binaries/Microsoft.Workflow.Compiler/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/windows-sysmon.log
@@ -32868,11 +34836,13 @@ Although unlikely, limited instances have been identified coming from native Mic
#### Reference
+
* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/windows-sysmon.log
@@ -32949,11 +34919,13 @@ Some legitimate applications may use a moved copy of msbuild.exe, triggering a f
#### Reference
+
* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log
@@ -33027,11 +34999,13 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg
#### Reference
+
* https://github.com/redcanaryco/AtomicTestHarnesses
* https://redcanary.com/blog/introducing-atomictestharnesses/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log
@@ -33105,6 +35079,7 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg
#### Reference
+
* https://codewhitesec.blogspot.com/2018/07/lethalhta.html
* https://github.com/redcanaryco/AtomicTestHarnesses
@@ -33112,6 +35087,7 @@ Although unlikely, some legitimate applications may exhibit this behavior, trigg
* https://redcanary.com/blog/introducing-atomictestharnesses/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log
@@ -33397,9 +35373,11 @@ Administrators debugging servers
#### Reference
+
* https://oscp.infosecsanyam.in/priv-escalation/windows-priv-escalation
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/atomic_red_team/windows-sysmon.log
@@ -33865,6 +35843,7 @@ Unknown. Tune out child processes as needed to limit volume of false positives.
#### Reference
+
* https://www.volexity.com/blog/2021/03/02/active-exploitation-of-microsoft-exchange-zero-day-vulnerabilities/
* https://www.microsoft.com/security/blog/2021/03/02/hafnium-targeting-exchange-servers/
@@ -33872,6 +35851,7 @@ Unknown. Tune out child processes as needed to limit volume of false positives.
* https://blog.rapid7.com/2021/03/03/rapid7s-insightidr-enables-detection-and-response-to-microsoft-exchange-0-day/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/windows-sysmon_umservices.log
@@ -34008,9 +35988,11 @@ Other tools could load images into LSASS for legitimate reason. But enterprise t
#### Reference
+
* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf
+
#### Test Dataset
@@ -34415,9 +36397,11 @@ Baseline your environment before production. It is possible build systems using
#### Reference
+
* https://www.microsoft.com/security/blog/2020/02/04/ghost-in-the-shell-investigating-web-shell-attacks/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/windows-sysmon.log
@@ -34491,6 +36475,7 @@ Administrators may modify the boot configuration.
#### Reference
+
* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md
* https://thedfirreport.com/2020/10/08/ryuks-return/
@@ -34500,6 +36485,7 @@ Administrators may modify the boot configuration.
* https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/wbadmin
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log
@@ -34785,11 +36771,13 @@ As is common with many fraud-related searches, we are usually looking to attribu
#### Reference
+
* https://splunkbase.splunk.com/app/2734/
* https://splunkbase.splunk.com/app/1809/
+
#### Test Dataset
@@ -34854,6 +36842,7 @@ As is common with many fraud-related searches, we are usually looking to attribu
#### Reference
+
* https://en.wikipedia.org/wiki/Session_ID
* https://en.wikipedia.org/wiki/Session_(computer_science)
@@ -34863,6 +36852,7 @@ As is common with many fraud-related searches, we are usually looking to attribu
* https://splunkbase.splunk.com/app/1809/
+
#### Test Dataset
@@ -34920,6 +36910,7 @@ As is common with many fraud-related searches, we are usually looking to attribu
#### Reference
+
* https://en.wikipedia.org/wiki/Session_ID
* https://en.wikipedia.org/wiki/Session_(computer_science)
@@ -34929,6 +36920,7 @@ As is common with many fraud-related searches, we are usually looking to attribu
* https://splunkbase.splunk.com/app/1809/
+
#### Test Dataset
@@ -35002,6 +36994,186 @@ Some of these processes may be used legitimately on web servers during maintenan
#### Test Dataset
+_version_: 1
+
+
+---
+
+### WinEvent Scheduled Task Created Within Public Path
+The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed from a user writeable file path.\
+The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not.\
+schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`.\
+The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory.\
+Upon triage, identify the task scheduled source. Was it schtasks.exe or was it via TaskService. Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
+- **Datamodel**:
+- **ATT&CK**: [T1053.005](https://attack.mitre.org/techniques/T1053.005/)
+- **Last Updated**: 2021-04-08
+
+
+ details
+
+#### Search
+```
+`wineventlog_security` EventCode=4698
+| xmlkv Message
+| search Command IN ("*\\users\\public\\*", "*\\programdata\\*", "*\\temp\\*", "*\\Windows\\Tasks\\*", "*\\appdata\\*")
+| stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `winevent_scheduled_task_created_within_public_path_filter`
+```
+#### Associated Analytic Story
+
+* Windows Persistence Techniques
+
+* Ransomware
+
+* Ryuk Ransomware
+
+
+#### How To Implement
+To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required.
+
+#### Required field
+
+* _time
+
+* dest
+
+* Task_Name
+
+* Description
+
+* Command
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation |
+
+
+#### Kill Chain Phase
+
+* Privilege Escalation
+
+
+#### Known False Positives
+False positives are possible if legitimate applications are allowed to register tasks in public paths. Filter as needed based on paths that are used legitimately.
+
+#### Reference
+
+
+* https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/
+
+* https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4698
+
+* https://redcanary.com/threat-detection-report/techniques/scheduled-task-job/
+
+* https://docs.microsoft.com/en-us/windows/win32/taskschd/time-trigger-example--scripting-?redirectedfrom=MSDN
+
+* https://app.any.run/tasks/e26f1b2e-befa-483b-91d2-e18636e2faf3/
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/taskschedule/windows-security.log
+
+
+_version_: 1
+
+
+---
+
+### WinEvent Scheduled Task Created to Spawn Shell
+The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed with a native Windows shell (PowerShell, Cmd, Wscript, Cscript).\
+The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not.\
+schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`.\
+The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory.\
+Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
+- **Datamodel**:
+- **ATT&CK**: [T1053.005](https://attack.mitre.org/techniques/T1053.005/)
+- **Last Updated**: 2021-04-12
+
+
+ details
+
+#### Search
+```
+`wineventlog_security` EventCode=4698
+| xmlkv Message
+| search Command IN ("*powershell.exe*", "*wscript.exe*", "*cscript.exe*", "*cmd.exe*", "*sh.exe*", "*ksh.exe*", "*zsh.exe*", "*bash.exe*", "*scrcons.exe*", "*pwsh.exe*")
+| stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `winevent_scheduled_task_created_to_spawn_shell_filter`
+```
+#### Associated Analytic Story
+
+* Windows Persistence Techniques
+
+* Ransomware
+
+* Ryuk Ransomware
+
+
+#### How To Implement
+To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required.
+
+#### Required field
+
+* _time
+
+* dest
+
+* Task_Name
+
+* Description
+
+* Command
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation |
+
+
+#### Kill Chain Phase
+
+* Privilege Escalation
+
+
+#### Known False Positives
+False positives are possible if legitimate applications are allowed to register tasks that call a shell to be spawned. Filter as needed based on command-line or processes that are used legitimately.
+
+#### Reference
+
+
+* https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/
+
+* https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4698
+
+* https://redcanary.com/threat-detection-report/techniques/scheduled-task-job/
+
+* https://docs.microsoft.com/en-us/windows/win32/taskschd/time-trigger-example--scripting-?redirectedfrom=MSDN
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-security.log
+
+
_version_: 1
@@ -35074,11 +37246,13 @@ administrators rarely use adfind, usually not used for legitimate reasons
#### Reference
+
* https://www.volexity.com/blog/2020/12/14/dark-halo-leverages-solarwinds-compromise-to-breach-organizations/
* https://www.fireeye.com/blog/threat-research/2019/01/a-nasty-trick-from-credential-theft-malware-to-business-disruption.html
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/atomic_red_team/windows-sysmon.log
@@ -35152,9 +37326,11 @@ It is unusual to turn this feature off a Windows system since it is a default se
#### Reference
+
* https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/
+
#### Test Dataset
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log
@@ -35416,6 +37592,246 @@ There may be legitimate reasons for system administrators to add entries to this
#### Test Dataset
+_version_: 1
+
+
+---
+
+### Winword Spawning Cmd
+The following detection identifies Microsoft Word spawning `cmd.exe`. Typically, this is not common behavior and not default with winword.exe. Winword.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). Cmd.exe spawning from winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line will indicate what is being executed. During triage, review parallel processes and identify any files that may have been written. It is possible that COM is utilized to trampoline the child process to `explorer.exe` or `wmiprvse.exe`.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
+- **Datamodel**: Endpoint
+- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566.001/)
+- **Last Updated**: 2021-04-22
+
+
+ details
+
+#### Search
+```
+
+| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=winword.exe Processes.process_name=cmd.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+| `drop_dm_object_name(Processes)`
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `winword_spawning_cmd_filter`
+```
+#### Associated Analytic Story
+
+* Spearphishing Attachments
+
+
+#### How To Implement
+To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
+
+#### Required field
+
+* _time
+
+* process_name
+
+* process_id
+
+* parent_process_name
+
+* dest
+
+* user
+
+* parent_process_id
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1566.001 | Spearphishing Attachment | Initial Access |
+
+
+#### Kill Chain Phase
+
+* Exploitation
+
+
+#### Known False Positives
+False positives should be limited, but if any are present, filter as needed.
+
+#### Reference
+
+
+* https://app.any.run/tasks/73af0064-a785-4c0a-ab0d-cde593fe16ef/
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log
+
+
+_version_: 1
+
+
+---
+
+### Winword Spawning PowerShell
+The following detection identifies Microsoft Word spawning PowerShell. Typically, this is not common behavior and not default with winword.exe. Winword.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). PowerShell spawning from winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command executed will most likely be encoded and captured via another detection. During triage, review parallel processes and identify any files that may have been written.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
+- **Datamodel**: Endpoint
+- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566.001/)
+- **Last Updated**: 2021-04-12
+
+
+ details
+
+#### Search
+```
+
+| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="winword.exe" Processes.process_name IN ("powershell.exe", "pwsh.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+| `drop_dm_object_name(Processes)`
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `winword_spawning_powershell_filter`
+```
+#### Associated Analytic Story
+
+* Spearphishing Attachments
+
+
+#### How To Implement
+To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
+
+#### Required field
+
+* _time
+
+* process_name
+
+* process_id
+
+* parent_process_name
+
+* dest
+
+* user
+
+* parent_process_id
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1566.001 | Spearphishing Attachment | Initial Access |
+
+
+#### Kill Chain Phase
+
+* Exploitation
+
+
+#### Known False Positives
+False positives should be limited, but if any are present, filter as needed.
+
+#### Reference
+
+
+* https://redcanary.com/threat-detection-report/techniques/powershell/
+
+* https://attack.mitre.org/techniques/T1566/001/
+
+* https://app.any.run/tasks/b79fa381-f35c-4b3e-8d02-507e7ee7342f/
+
+* https://app.any.run/tasks/181ac90b-0898-4631-8701-b778a30610ad/
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log
+
+
+_version_: 1
+
+
+---
+
+### Winword Spawning Windows Script Host
+The following detection identifies Microsoft Winword.exe spawning Windows Script Host - `cscript.exe` or `wscript.exe`. Typically, this is not common behavior and not default with Winword.exe. Winword.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). `cscript.exe` or `wscript.exe` default location is `c:\windows\system32\` or c:windows\syswow64\`. `cscript.exe` or `wscript.exe` spawning from Winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line executed will most likely be obfuscated and captured via another detection. During triage, review parallel processes and identify any files that may have been written. Review the reputation of the remote destination and block accordingly.
+
+- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
+- **Datamodel**: Endpoint
+- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566.001/)
+- **Last Updated**: 2021-04-12
+
+
+ details
+
+#### Search
+```
+
+| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="winword.exe" Processes.process_name IN ("cscript.exe", "wscript.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+| `drop_dm_object_name(Processes)`
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `winword_spawning_windows_script_host_filter`
+```
+#### Associated Analytic Story
+
+* Spearphishing Attachment
+
+
+#### How To Implement
+To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
+
+#### Required field
+
+* _time
+
+* process_name
+
+* process_id
+
+* parent_process_name
+
+* dest
+
+* user
+
+* parent_process_id
+
+
+
+#### ATT&CK
+
+| ID | Technique | Tactic |
+| ----------- | ----------- |--------------|
+| T1566.001 | Spearphishing Attachment | Initial Access |
+
+
+#### Kill Chain Phase
+
+* Exploitation
+
+
+#### Known False Positives
+There will be limited false positives and it will be different for every environment. Tune by child process or command-line as needed.
+
+#### Reference
+
+
+* https://attack.mitre.org/techniques/T1566/001/
+
+
+
+#### Test Dataset
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_wsh.log
+
+
_version_: 1
@@ -35857,11 +38273,13 @@ GCP Oauth token abuse detection will only work if there are access policies in p
#### Reference
+
* https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-1
* https://www.netskope.com/blog/gcp-oauth-token-hijacking-in-google-cloud-part-2
+
#### Test Dataset
diff --git a/docs/detections.wiki b/docs/detections.wiki
index 0d91b55f86..9df5e59f44 100644
--- a/docs/detections.wiki
+++ b/docs/detections.wiki
@@ -1072,11 +1072,13 @@ While this search has no known false positives, it is possible that an AWS admin
====Reference====
+
* https://labs.bishopfox.com/tech-blog/privilege-escalation-in-aws
* https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/
+
====Test Dataset====
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_create_policy_version/aws_cloudtrail_events.json
@@ -1151,11 +1153,13 @@ While this search has no known false positives, it is possible that an AWS admin
====Reference====
+
* https://labs.bishopfox.com/tech-blog/privilege-escalation-in-aws
* https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/
+
====Test Dataset====
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_createaccesskey/aws_cloudtrail_events.json
@@ -1234,11 +1238,13 @@ While this search has no known false positives, it is possible that an AWS admin
====Reference====
+
* https://labs.bishopfox.com/tech-blog/privilege-escalation-in-aws
* https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/
+
====Test Dataset====
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_createloginprofile/aws_cloudtrail_events.json
@@ -1393,6 +1399,7 @@ unknown
====Reference====
+
* https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/
* https://github.com/d1vious/git-wild-hunt
@@ -1400,6 +1407,7 @@ unknown
* https://www.youtube.com/watch?v=PgzNib37g0M
+
====Test Dataset====
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/aws_kms_key/aws_cloudtrail_events.json
@@ -1478,6 +1486,7 @@ bucket with S3 encryption
====Reference====
+
* https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/
* https://github.com/d1vious/git-wild-hunt
@@ -1485,6 +1494,7 @@ bucket with S3 encryption
* https://www.youtube.com/watch?v=PgzNib37g0M
+
====Test Dataset====
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/s3_file_encryption/aws_cloudtrail_events.json
@@ -1561,14 +1571,429 @@ While this search has no known false positives.
====Reference====
+
* https://github.com/aquasecurity/cloudsploit
+
====Test Dataset====
* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1526/aws_security_scanner/aws_security_scanner.json
+''version'': 1
+
+
+
+----
+
+===Aws iam accessdenied discovery events===
+The following detection identifies excessive AccessDenied events within an hour timeframe. It is possible that an access key to AWS may have been stolen and is being misused to perform discovery events. In these instances, the access is not available with the key stolen therefore these events will be generated.
+
+* '''Product''': Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud, Splunk Security Analytics for AWS
+* '''Datamodel''':
+* '''ATT&CK''': [https://attack.mitre.org/techniques/T1580/ T1580]
+* '''Last Updated''': 2021-04-05
+
+
+
+
+====Search====
+`cloudtrail` (errorCode = "AccessDenied") user_type=IAMUser (userAgent!=*.amazonaws.com)
+| bucket _time span=1h
+| stats count as failures min(_time) as firstTime max(_time) as lastTime, dc(eventName) as methods, dc(eventSource) as sources values(userIdentity.arn) by src_ip, userIdentity.arn, _time
+| where failures >= 5 and methods >= 1 and sources >= 1
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `aws_iam_accessdenied_discovery_events_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#Suspicious_Cloud_User_Activities|Suspicious Cloud User Activities]]
+
+
+====How To Implement====
+The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
+
+====Required field====
+
+* _time
+
+* eventName
+
+* eventSource
+
+* userAgent
+
+* errorCode
+
+* userIdentity.type
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1580
+| Cloud Infrastructure Discovery
+| Discovery
+|}
+
+
+====Kill Chain Phase====
+
+* Reconnaissance
+
+
+====Known False Positives====
+It is possible to start this detection will need to be tuned by source IP or user. In addition, change the count values to an upper threshold to restrict false positives.
+
+====Reference====
+
+
+* https://aws.amazon.com/premiumsupport/knowledge-center/troubleshoot-iam-permission-errors/
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_iam_accessdenied_discovery_events/aws_iam_accessdenied_discovery_events.json
+
+
+''version'': 1
+
+
+
+
+====Search====
+`cloudtrail` (errorCode=MalformedPolicyDocumentException) status=failure (userAgent!=*.amazonaws.com)
+| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyName) as policy_name by src eventName eventSource aws_account_id errorCode requestParameters.policyDocument userAgent eventID awsRegion userIdentity.principalId user_arn
+| where count >= 2
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `aws_iam_assume_role_policy_brute_force_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#AWS_IAM_Privilege_Escalation|AWS IAM Privilege Escalation]]
+
+
+====How To Implement====
+The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs. Set the `where count` greater than a value to identify suspicious activity in your environment.
+
+====Required field====
+
+* _time
+
+* eventName
+
+* userAgent
+
+* errorCode
+
+* requestParameters.policyName
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1580
+| Cloud Infrastructure Discovery
+| Discovery
+|-
+| T1110
+| Brute Force
+| Credential Access
+|}
+
+
+====Kill Chain Phase====
+
+* Reconnaissance
+
+
+====Known False Positives====
+This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users.
+
+====Reference====
+
+
+* https://www.praetorian.com/blog/aws-iam-assume-role-vulnerabilities
+
+* https://rhinosecuritylabs.com/aws/assume-worst-aws-assume-role-enumeration/
+
+* https://www.elastic.co/guide/en/security/current/aws-iam-brute-force-of-assume-role-policy.html
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_iam_assume_role_policy_brute_force/aws_iam_assume_role_policy_brute_force.json
+
+
+''version'': 1
+
+
+
+
+====Search====
+`cloudtrail` eventName=DeletePolicy (userAgent!=*.amazonaws.com)
+| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyArn) as policyArn by src eventName eventSource aws_account_id errorCode errorMessage userAgent eventID awsRegion userIdentity.principalId userIdentity.arn
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `aws_iam_delete_policy_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#AWS_IAM_Privilege_Escalation|AWS IAM Privilege Escalation]]
+
+
+====How To Implement====
+The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
+
+====Required field====
+
+* _time
+
+* eventName
+
+* userAgent
+
+* errorCode
+
+* requestParameters.policyArn
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1098
+| Account Manipulation
+| Persistence
+|}
+
+
+====Kill Chain Phase====
+
+* Actions on Objectives
+
+
+====Known False Positives====
+This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete policies (least privilege). In addition, this may be saved seperately and tuned for failed or success attempts only.
+
+====Reference====
+
+
+* https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeletePolicy.html
+
+* https://docs.aws.amazon.com/cli/latest/reference/iam/delete-policy.html
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_delete_policy/aws_iam_delete_policy.json
+
+
+''version'': 1
+
+
+
+
+====Search====
+`cloudtrail` eventSource=iam.amazonaws.com eventName=DeleteGroup errorCode IN (NoSuchEntityException,DeleteConflictException, AccessDenied) (userAgent!=*.amazonaws.com)
+| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.groupName) as group_name by src eventName eventSource aws_account_id errorCode errorMessage userAgent eventID awsRegion userIdentity.principalId user_arn
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `aws_iam_failure_group_deletion_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#AWS_IAM_Privilege_Escalation|AWS IAM Privilege Escalation]]
+
+
+====How To Implement====
+The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
+
+====Required field====
+
+* _time
+
+* eventName
+
+* userAgent
+
+* errorCode
+
+* requestParameters.groupName
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1098
+| Account Manipulation
+| Persistence
+|}
+
+
+====Kill Chain Phase====
+
+* Actions on Objectives
+
+
+====Known False Positives====
+This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege).
+
+====Reference====
+
+
+* https://awscli.amazonaws.com/v2/documentation/api/latest/reference/iam/delete-group.html
+
+* https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeleteGroup.html
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_failure_group_deletion/aws_iam_failure_group_deletion.json
+
+
+''version'': 1
+
+
+
+
+====Search====
+`cloudtrail` eventSource=iam.amazonaws.com eventName=DeleteGroup errorCode=success (userAgent!=*.amazonaws.com)
+| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.groupName) by src eventName eventSource errorCode user_agent awsRegion userIdentity.principalId user_arn
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `aws_iam_successful_group_deletion_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#AWS_IAM_Privilege_Escalation|AWS IAM Privilege Escalation]]
+
+
+====How To Implement====
+The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.
+
+====Required field====
+
+* _time
+
+* eventName
+
+* userAgent
+
+* errorCode
+
+* requestParameters.groupName
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1069.003
+| Cloud Groups
+| Discovery
+|-
+| T1098
+| Account Manipulation
+| Persistence
+|}
+
+
+====Kill Chain Phase====
+
+* Actions on Objectives
+
+
+====Known False Positives====
+This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege).
+
+====Reference====
+
+
+* https://awscli.amazonaws.com/v2/documentation/api/latest/reference/iam/delete-group.html
+
+* https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeleteGroup.html
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_successful_group_deletion/aws_iam_successful_group_deletion.json
+
+
''version'': 1
+
+
+====Search====
+
+| from read_ssa_enriched_events()
+| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null))
+| eval parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), parent_process_name=mvindex(split(parent_process, "\\"), -1), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null)
+| where parent_process_name!=null
+| select parent_process_name, process_name, timestamp, dest_device_id, dest_user_id
+| conditional_anomaly conditional="parent_process_name" target="process_name"
+| rename output as input
+| where input < 1
+| adaptive_threshold algorithm="quantile" entity="parent_process_name" window=604800000L
+| where label AND quantile<0.1 AND (process_name="powershell.exe" OR process_name="regsvcs.exe" OR process_name="ftp.exe" OR process_name="dfsvc.exe" OR process_name="rasautou.exe" OR process_name="schtasks.exe" OR process_name="xwizard.exe" OR process_name="findstr.exe" OR process_name="esentutl.exe" OR process_name="cscript.exe" OR process_name="reg.exe" OR process_name="csc.exe" OR process_name="atbroker.exe" OR process_name="print.exe" OR process_name="pcwrun.exe" OR process_name="vbc.exe" OR process_name="rpcping.exe" OR process_name="wsreset.exe" OR process_name="ilasm.exe" OR process_name="certutil.exe" OR process_name="replace.exe" OR process_name="mshta.exe" OR process_name="bitsadmin.exe" OR process_name="wscript.exe" OR process_name="ieexec.exe" OR process_name="cmd.exe" OR process_name="microsoft.workflow.compiler.exe" OR process_name="runscripthelper.exe" OR process_name="makecab.exe" OR process_name="forfiles.exe" OR process_name="desktopimgdownldr.exe" OR process_name="control.exe" OR process_name="msbuild.exe" OR process_name="register-cimprovider.exe" OR process_name="tttracer.exe" OR process_name="ie4uinit.exe" OR process_name="sc.exe" OR process_name="bash.exe" OR process_name="hh.exe" OR process_name="cmstp.exe" OR process_name="mmc.exe" OR process_name="jsc.exe" OR process_name="scriptrunner.exe" OR process_name="odbcconf.exe" OR process_name="extexport.exe" OR process_name="msdt.exe" OR process_name="diskshadow.exe" OR process_name="extrac32.exe" OR process_name="eventvwr.exe" OR process_name="mavinject.exe" OR process_name="regasm.exe" OR process_name="gpscript.exe" OR process_name="rundll32.exe" OR process_name="regsvr32.exe" OR process_name="regedit.exe" OR process_name="msiexec.exe" OR process_name="gfxdownloadwrapper.exe" OR process_name="presentationhost.exe" OR process_name="regini.exe" OR process_name="wmic.exe" OR process_name="runonce.exe" OR process_name="syncappvpublishingserver.exe" OR process_name="verclsid.exe" OR process_name="psr.exe" OR process_name="infdefaultinstall.exe" OR process_name="explorer.exe" OR process_name="expand.exe" OR process_name="installutil.exe" OR process_name="netsh.exe" OR process_name="wab.exe" OR process_name="dnscmd.exe" OR process_name="at.exe" OR process_name="pcalua.exe" OR process_name="cmdkey.exe" OR process_name="msconfig.exe")
+
+| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD"
+| into write_null();
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#Unusual_Processes|Unusual Processes]]
+
+
+====How To Implement====
+Collect endpoint data such as sysmon or 4688 events.
+
+====Required field====
+
+* process_name
+
+* parent_process_name
+
+* _time
+
+* dest_device_id
+
+* dest_user_id
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1203
+| Exploitation for Client Execution
+| Execution
+|-
+| T1059
+| Command and Scripting Interpreter
+| Execution
+|-
+| T1053
+| Scheduled Task/Job
+| Execution, Persistence, Privilege Escalation
+|-
+| T1072
+| Software Deployment Tools
+| Execution, Lateral Movement
+|}
+
+
+====Kill Chain Phase====
+
+* Exploitation
+
+
+====Known False Positives====
+Some custom tools used by admins could be used rarely to launch remotely applications. This might trigger false positives at the beginning when it hasn't collected yet enough data to construct the baseline.
+
+
+====Reference====
+
+
+====Test Dataset====
+
+
+''version'': 1
+
+
-
-
-====Search====
-
-| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_key_name=ExecutionPolicy (Registry.registry_value_name=Unrestricted OR Registry.registry_value_name=Bypass) by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest
-| `drop_dm_object_name(Registry)`
-| `security_content_ctime(firstTime)`
-|`security_content_ctime(lastTime)`
-| `attempt_to_set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter`
-
-====Associated Analytic Story====
-
-* [[Documentation:ESSOC:stories:UseCase#Malicious_PowerShell|Malicious PowerShell]]
-
-* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]]
-
-* [[Documentation:ESSOC:stories:UseCase#HAFNIUM_Group|HAFNIUM Group]]
-
-
-====How To Implement====
-You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Registry node. You must also be ingesting logs with the fields registry_path, registry_key_name, and registry_value_name from your endpoints.
-
-====Required field====
-
-* _time
-
-* Registry.registry_path
-
-* Registry.registry_key_name
-
-* Registry.registry_value_name
-
-* Registry.dest
-
-
-
-====ATT&CK====
-{|
-! style="text-align:left;"| ID
-! Technique
-! Tactic
-|-
-| T1059.001
-| PowerShell
-| Execution
-|}
-
-
-====Kill Chain Phase====
-
-* Installation
-
-* Actions on Objectives
-
-
-====Known False Positives====
-Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate.
-
-====Reference====
-
-
-====Test Dataset====
-
-* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_execution_policy/windows-sysmon.log
-
-
''version'': 6
+
+
+====Search====
+
+| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=dllhost.exe by _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name
+| `drop_dm_object_name(Processes)`
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| regex process="(dllhost\.exe.{0,4}$)"
+| join process_id [
+| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port
+| `drop_dm_object_name(Ports)`
+| rename dest as connection_to_CNC]
+| table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port
+| `dllhost_with_no_command_line_arguments_with_network_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#Cobalt_Strike|Cobalt Strike]]
+
+
+====How To Implement====
+To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `port` node.
+
+====Required field====
+
+* _time
+
+* EventID
+
+* process_name
+
+* process_id
+
+* parent_process_name
+
+* dest_port
+
+* process_path
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1055
+| Process Injection
+| Defense Evasion, Privilege Escalation
+|}
+
+
+====Kill Chain Phase====
+
+* Exploitation
+
+
+====Known False Positives====
+Although unlikely, some legitimate third party applications may use a moved copy of dllhost, triggering a false positive.
+
+====Reference====
+
+
+* https://raw.githubusercontent.com/threatexpress/malleable-c2/c3385e481159a759f79b8acfe11acf240893b830/jquery-c2.4.2.profile
+
+* https://blog.cobaltstrike.com/2021/02/09/learn-pipe-fitting-for-all-of-your-offense-projects/
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon_dllhost.log
+
+
''version'': 1
+
+
+====Search====
+
+| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="excel.exe" Processes.process_name IN ("powershell.exe", "pwsh.exe") by Processes.parent_process Processes.process_name Processes.user Processes.dest
+| `drop_dm_object_name("Processes")`
+| `security_content_ctime(firstTime)`
+|`security_content_ctime(lastTime)`
+| `excel_spawning_powershell_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#Spearphishing_Attachments|Spearphishing Attachments]]
+
+
+====How To Implement====
+To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
+
+====Required field====
+
+* _time
+
+* process_name
+
+* process_id
+
+* parent_process_name
+
+* dest
+
+* user
+
+* parent_process_id
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1003.002
+| Security Account Manager
+| Credential Access
+|}
+
+
+====Kill Chain Phase====
+
+* Exploitation
+
+
+====Known False Positives====
+False positives should be limited, but if any are present, filter as needed.
+
+====Reference====
+
+
+* https://redcanary.com/threat-detection-report/techniques/powershell/
+
+* https://attack.mitre.org/techniques/T1566/001/
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log
+
+
+''version'': 1
+
+
+
+
+====Search====
+
+| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="excel.exe" Processes.process_name IN ("cscript.exe", "wscript.exe") by Processes.parent_process Processes.process_name Processes.user Processes.dest
+| `drop_dm_object_name("Processes")`
+| `security_content_ctime(firstTime)`
+|`security_content_ctime(lastTime)`
+| `excel_spawning_windows_script_host_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#Spearphishing_Attachments|Spearphishing Attachments]]
+
+
+====How To Implement====
+To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
+
+====Required field====
+
+* _time
+
+* process_name
+
+* process_id
+
+* parent_process_name
+
+* dest
+
+* user
+
+* parent_process_id
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1003.002
+| Security Account Manager
+| Credential Access
+|}
+
+
+====Kill Chain Phase====
+
+* Exploitation
+
+
+====Known False Positives====
+False positives should be limited, but if any are present, filter as needed. In some instances, `cscript.exe` is used for legitimate business practices.
+
+====Reference====
+
+
+* https://app.any.run/tasks/8ecfbc29-03d0-421c-a5bf-3905d29192a2/
+
+* https://attack.mitre.org/techniques/T1566/001/
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log
+
+
''version'': 1
+
+
+====Search====
+
+| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=gpupdate.exe by _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name
+| `drop_dm_object_name(Processes)`
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| regex process="(gpupdate\.exe.{0,4}$)"
+| join process_id [
+| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port
+| `drop_dm_object_name(Ports)`
+| rename dest as connection_to_CNC]
+| table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port
+| `gpupdate_with_no_command_line_arguments_with_network_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#Cobalt_Strike|Cobalt Strike]]
+
+
+====How To Implement====
+To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
+
+====Required field====
+
+* _time
+
+* EventID
+
+* process_name
+
+* process_id
+
+* parent_process_name
+
+* dest_port
+
+* process_path
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1055
+| Process Injection
+| Defense Evasion, Privilege Escalation
+|}
+
+
+====Kill Chain Phase====
+
+* Exploitation
+
+
+====Known False Positives====
+Limited false positives may be present in small environments. Tuning may be required based on parent process.
+
+====Reference====
+
+
+* https://raw.githubusercontent.com/xx0hcd/Malleable-C2-Profiles/0ef8cf4556e26f6d4190c56ba697c2159faa5822/crimeware/trick_ryuk.profile
+
+* https://blog.cobaltstrike.com/2021/02/09/learn-pipe-fitting-for-all-of-your-offense-projects/
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
+
+
''version'': 1
+
+
+====Search====
+
+| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "winword.exe" OR Processes.parent_process_name = "excel.exe" OR Processes.parent_process_name = "powerpnt.exe" OR Processes.parent_process_name = "wordpad.exe") Processes.process_name=rundll32.exe by Processes.parent_process Processes.process_name Processes.process_id Processes.process_guid Processes.user Processes.dest
+| `drop_dm_object_name("Processes")`
+| `security_content_ctime(firstTime)`
+|`security_content_ctime(lastTime)`
+| `office_application_spawn_rundll32_process_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#Spearphishing_Attachments|Spearphishing Attachments]]
+
+
+====How To Implement====
+To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
+
+====Required field====
+
+* Processes.process
+
+* Processes.parent_process_name
+
+* _time
+
+* Processes.process_name
+
+* Processes.dest
+
+* Processes.user
+
+* Processes.process_id
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1566.001
+| Spearphishing Attachment
+| Initial Access
+|}
+
+
+====Kill Chain Phase====
+
+* Exploitation
+
+
+====Known False Positives====
+unknown
+
+====Reference====
+
+
+* https://any.run/malware-trends/trickbot
+
+* https://any.run/report/47561b4e949041eff0a0f4693c59c81726591779fe21183ae9185b5eb6a69847/aba3722a-b373-4dae-8273-8730fb40cdbe
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log
+
+
+''version'': 1
+
+
+
+
+====Search====
+`sysmon` EventCode=7 process_name IN ("WINWORD.EXE", "EXCEL.EXE", "POWERPNT.EXE", "wordpad.exe") ImageLoaded = "*\\taskschd.dll"
+| stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) as AllImageLoaded count by Computer EventCode Image process_name ProcessId ProcessGuid
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `office_document_creating_schedule_task_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#Spearphishing_Attachments|Spearphishing Attachments]]
+
+
+====How To Implement====
+To successfully implement this search, you need to be ingesting logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Also be sure to include those monitored dll to your own sysmon config.
+
+====Required field====
+
+* ImageLoaded
+
+* AllImageLoaded
+
+* Computer
+
+* EventCode
+
+* Image
+
+* process_name
+
+* ProcessId
+
+* ProcessGuid
+
+* _time
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1566.001
+| Spearphishing Attachment
+| Initial Access
+|}
+
+
+====Kill Chain Phase====
+
+* Exploitation
+
+
+====Known False Positives====
+unknown
+
+====Reference====
+
+
+* https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/
+
+* https://redcanary.com/threat-detection-report/techniques/scheduled-task-job/
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log
+
+
+''version'': 1
+
+
+
+
+====Search====
+`sysmon` EventCode=7 process_name IN ("WINWORD.EXE", "EXCEL.EXE", "POWERPNT.EXE", "wordpad.exe") ImageLoaded IN ("*\\VBE7INTL.DLL","*\\VBE7.DLL", "*\\VBEUI.DLL")
+| stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) as AllImageLoaded count by Computer EventCode Image process_name ProcessId ProcessGuid
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `office_document_executing_macro_code_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#Spearphishing_Attachments|Spearphishing Attachments]]
+
+
+====How To Implement====
+To successfully implement this search, you need to be ingesting logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Also be sure to include those monitored dll to your own sysmon config.
+
+====Required field====
+
+* ImageLoaded
+
+* AllImageLoaded
+
+* Computer
+
+* EventCode
+
+* Image
+
+* process_name
+
+* ProcessId
+
+* ProcessGuid
+
+* _time
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1566.001
+| Spearphishing Attachment
+| Initial Access
+|}
+
+
+====Kill Chain Phase====
+
+* Exploitation
+
+
+====Known False Positives====
+Normal Office Document macro use for automation
+
+====Reference====
+
+
+* https://www.joesandbox.com/analysis/386500/0/html
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log
+
+
+''version'': 1
+
+
+
+
+====Search====
+`sysmon` EventCode=1 parent_process_name IN ("powerpnt.exe", "winword.exe", "excel.exe", "visio.exe", "wordpad.exe") process_name = "*.exe" cmdline IN ("*http:*","*https:*") NOT(OriginalFileName IN("*\\firefox.exe", "*\\chrome.exe","*\\iexplore.exe","*\\msedge.exe"))
+| stats min(_time) as firstTime max(_time) as lastTime count by parent_process_name process_name parent_process cmdline process_id OriginalFileName ProcessGuid Computer EventCode
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `office_document_spawned_child_process_to_download_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#Spearphishing_Attachments|Spearphishing Attachments]]
+
+
+====How To Implement====
+To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances office application and browser may be used.
+
+====Required field====
+
+* _time
+
+* parent_process_name
+
+* process_name
+
+* parent_process
+
+* cmdline
+
+* process_id
+
+* OriginalFileName
+
+* ProcessGuid
+
+* Computer
+
+* EventCode
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1566.001
+| Spearphishing Attachment
+| Initial Access
+|}
+
+
+====Kill Chain Phase====
+
+* Exploitation
+
+
+====Known False Positives====
+default browser not in the filter list
+
+====Reference====
+
+
+* https://app.any.run/tasks/92d7ef61-bfd7-4c92-bc15-322172b4ebec/#
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets2/windows-sysmon.log
+
+
+''version'': 1
+
+
+
+
+====Search====
+
+| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=rundll32.exe (Processes.process!=*.dll*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+| `drop_dm_object_name(Processes)`
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `office_product_spawning_rundll32_with_no_dll_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#Spearphishing_Attachments|Spearphishing Attachments]]
+
+
+====How To Implement====
+To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
+
+====Required field====
+
+* _time
+
+* process_name
+
+* process_id
+
+* parent_process_name
+
+* dest
+
+* user
+
+* parent_process_id
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1566.001
+| Spearphishing Attachment
+| Initial Access
+|}
+
+
+====Kill Chain Phase====
+
+* Exploitation
+
+
+====Known False Positives====
+False positives should be limited, but if any are present, filter as needed.
+
+====Reference====
+
+
+* https://www.joesandbox.com/analysis/395471/0/html
+
+* https://app.any.run/tasks/cef4b8ba-023c-4b3b-b2ef-6486a44f6ed9/
+
+* https://any.run/malware-trends/icedid
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_icedid.log
+
+
''version'': 1
-
-
-====Search====
-
-| from read_ssa_enriched_events()
-| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null))
-| eval parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), parent_process_name=mvindex(split(parent_process, "\\"), -1), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null)
-| where parent_process_name!=null
-| select parent_process_name, process_name, timestamp, dest_device_id, dest_user_id
-| conditional_anomaly conditional="parent_process_name" target="process_name"
-| rename output as input
-| where input < 1
-| adaptive_threshold algorithm="quantile" entity="parent_process_name" window=604800000L
-| where label AND quantile<0.1 AND (process_name="powershell.exe" OR process_name="regsvcs.exe" OR process_name="ftp.exe" OR process_name="dfsvc.exe" OR process_name="rasautou.exe" OR process_name="schtasks.exe" OR process_name="xwizard.exe" OR process_name="findstr.exe" OR process_name="esentutl.exe" OR process_name="cscript.exe" OR process_name="reg.exe" OR process_name="csc.exe" OR process_name="atbroker.exe" OR process_name="print.exe" OR process_name="pcwrun.exe" OR process_name="vbc.exe" OR process_name="rpcping.exe" OR process_name="wsreset.exe" OR process_name="ilasm.exe" OR process_name="certutil.exe" OR process_name="replace.exe" OR process_name="mshta.exe" OR process_name="bitsadmin.exe" OR process_name="wscript.exe" OR process_name="ieexec.exe" OR process_name="cmd.exe" OR process_name="microsoft.workflow.compiler.exe" OR process_name="runscripthelper.exe" OR process_name="makecab.exe" OR process_name="forfiles.exe" OR process_name="desktopimgdownldr.exe" OR process_name="control.exe" OR process_name="msbuild.exe" OR process_name="register-cimprovider.exe" OR process_name="tttracer.exe" OR process_name="ie4uinit.exe" OR process_name="sc.exe" OR process_name="bash.exe" OR process_name="hh.exe" OR process_name="cmstp.exe" OR process_name="mmc.exe" OR process_name="jsc.exe" OR process_name="scriptrunner.exe" OR process_name="odbcconf.exe" OR process_name="extexport.exe" OR process_name="msdt.exe" OR process_name="diskshadow.exe" OR process_name="extrac32.exe" OR process_name="eventvwr.exe" OR process_name="mavinject.exe" OR process_name="regasm.exe" OR process_name="gpscript.exe" OR process_name="rundll32.exe" OR process_name="regsvr32.exe" OR process_name="regedit.exe" OR process_name="msiexec.exe" OR process_name="gfxdownloadwrapper.exe" OR process_name="presentationhost.exe" OR process_name="regini.exe" OR process_name="wmic.exe" OR process_name="runonce.exe" OR process_name="syncappvpublishingserver.exe" OR process_name="verclsid.exe" OR process_name="psr.exe" OR process_name="infdefaultinstall.exe" OR process_name="explorer.exe" OR process_name="expand.exe" OR process_name="installutil.exe" OR process_name="netsh.exe" OR process_name="wab.exe" OR process_name="dnscmd.exe" OR process_name="at.exe" OR process_name="pcalua.exe" OR process_name="cmdkey.exe" OR process_name="msconfig.exe")
-
-| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = "TBD"
-| into write_null();
-
-====Associated Analytic Story====
-
-* [[Documentation:ESSOC:stories:UseCase#Unusual_Processes|Unusual Processes]]
-
-
-====How To Implement====
-Collect endpoint data such as sysmon or 4688 events.
-
-====Required field====
-
-* process_name
-
-* parent_process_name
-
-* _time
-
-* dest_device_id
-
-* dest_user_id
-
-
-
-====ATT&CK====
-{|
-! style="text-align:left;"| ID
-! Technique
-! Tactic
-|-
-| T1203
-| Exploitation for Client Execution
-| Execution
-|-
-| T1059
-| Command and Scripting Interpreter
-| Execution
-|-
-| T1053
-| Scheduled Task/Job
-| Execution, Persistence, Privilege Escalation
-|-
-| T1072
-| Software Deployment Tools
-| Execution, Lateral Movement
-|}
-
-
-====Kill Chain Phase====
-
-* Exploitation
-
-
-====Known False Positives====
-Some custom tools used by admins could be used rarely to launch remotely applications. This might trigger false positives at the beginning when it hasn't collected yet enough data to construct the baseline.
-
-
-====Reference====
-
-
-====Test Dataset====
-
-
''version'': 1
+
+
+====Search====
+
+| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe by _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name
+| `drop_dm_object_name(Processes)`
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| regex process="(rundll32\.exe.{0,4}$)"
+| join process_id [
+| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port
+| `drop_dm_object_name(Ports)`
+| rename dest as connection_to_CNC]
+| table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port
+| `rundll32_with_no_command_line_arguments_with_network_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#Suspicious_Rundll32_Activity|Suspicious Rundll32 Activity]]
+
+* [[Documentation:ESSOC:stories:UseCase#Cobalt_Strike|Cobalt Strike]]
+
+
+====How To Implement====
+To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `port` node.
+
+====Required field====
+
+* _time
+
+* EventID
+
+* process_name
+
+* process_id
+
+* parent_process_name
+
+* dest_port
+
+* process_path
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1218.011
+| Rundll32
+| Defense Evasion
+|}
+
+
+====Kill Chain Phase====
+
+* Exploitation
+
+
+====Known False Positives====
+Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive.
+
+====Reference====
+
+
+* https://attack.mitre.org/techniques/T1218/011/
+
+* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md
+
+* https://lolbas-project.github.io/lolbas/Binaries/Rundll32
+
+* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log
+
+
+''version'': 1
+
+
+
+
+====Search====
+
+| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=searchprotocolhost.exe by _time span=1h Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name
+| `drop_dm_object_name(Processes)`
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| regex process="(searchprotocolhost\.exe.{0,4}$)"
+| join process_id [
+| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port
+| `drop_dm_object_name(Ports)`
+| rename dest as connection_to_CNC]
+| table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port
+| `searchprotocolhost_with_no_command_line_with_network_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#Cobalt_Strike|Cobalt Strike]]
+
+
+====How To Implement====
+To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `ports` node.
+
+====Required field====
+
+* _time
+
+* process_name
+
+* process_id
+
+* parent_process_name
+
+* dest_port
+
+* process_path
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1055
+| Process Injection
+| Defense Evasion, Privilege Escalation
+|}
+
+
+====Kill Chain Phase====
+
+* Exploitation
+
+
+====Known False Positives====
+Limited false positives may be present in small environments. Tuning may be required based on parent process.
+
+====Reference====
+
+
+* https://github.com/fireeye/red_team_tool_countermeasures/blob/master/rules/PGF/supplemental/hxioc/SUSPICIOUS%20EXECUTION%20OF%20SEARCHPROTOCOLHOST%20(METHODOLOGY).ioc
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon_searchprotocolhost.log
+
+
+''version'': 1
+
+
+
+
+====Search====
+
+| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_key_name=ExecutionPolicy (Registry.registry_value_name=Unrestricted OR Registry.registry_value_name=Bypass) by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest
+| `drop_dm_object_name(Registry)`
+| `security_content_ctime(firstTime)`
+|`security_content_ctime(lastTime)`
+| `set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#Malicious_PowerShell|Malicious PowerShell]]
+
+* [[Documentation:ESSOC:stories:UseCase#Credential_Dumping|Credential Dumping]]
+
+* [[Documentation:ESSOC:stories:UseCase#HAFNIUM_Group|HAFNIUM Group]]
+
+
+====How To Implement====
+You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Registry node. You must also be ingesting logs with the fields registry_path, registry_key_name, and registry_value_name from your endpoints.
+
+====Required field====
+
+* _time
+
+* Registry.registry_path
+
+* Registry.registry_key_name
+
+* Registry.registry_value_name
+
+* Registry.dest
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1059.001
+| PowerShell
+| Execution
+|}
+
+
+====Kill Chain Phase====
+
+* Installation
+
+* Actions on Objectives
+
+
+====Known False Positives====
+Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate.
+
+====Reference====
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_execution_policy/windows-sysmon.log
+
+
+''version'': 6
+
+
+
+
+====Search====
+`wineventlog_security` EventCode=4698
+| xmlkv Message
+| search Arguments IN ("*http*")
+| stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden, Arguments
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `shedule_task_with_http_command_arguments_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#Windows_Persistence_Techniques|Windows Persistence Techniques]]
+
+
+====How To Implement====
+To successfully implement this search, you need to be ingesting logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and filter known instances of Task schedule used in your environment.
+
+====Required field====
+
+* _time
+
+* dest
+
+* Task_Name
+
+* Command
+
+* Author
+
+* Enabled
+
+* Hidden
+
+* Arguments
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1053
+| Scheduled Task/Job
+| Execution, Persistence, Privilege Escalation
+|}
+
+
+====Kill Chain Phase====
+
+* Exploitation
+
+
+====Known False Positives====
+unknown
+
+====Reference====
+
+
+* https://app.any.run/tasks/92d7ef61-bfd7-4c92-bc15-322172b4ebec/
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/tasksched/windows-security.log
+
+
''version'': 1
+
+
+====Search====
+`wineventlog_security` EventCode=4698
+| xmlkv Message
+| search Command IN ("*\\users\\public\\*", "*\\programdata\\*", "*\\temp\\*", "*\\Windows\\Tasks\\*", "*\\appdata\\*")
+| stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `winevent_scheduled_task_created_within_public_path_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#Windows_Persistence_Techniques|Windows Persistence Techniques]]
+
+* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]]
+
+* [[Documentation:ESSOC:stories:UseCase#Ryuk_Ransomware|Ryuk Ransomware]]
+
+
+====How To Implement====
+To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required.
+
+====Required field====
+
+* _time
+
+* dest
+
+* Task_Name
+
+* Description
+
+* Command
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1053.005
+| Scheduled Task
+| Execution, Persistence, Privilege Escalation
+|}
+
+
+====Kill Chain Phase====
+
+* Privilege Escalation
+
+
+====Known False Positives====
+False positives are possible if legitimate applications are allowed to register tasks in public paths. Filter as needed based on paths that are used legitimately.
+
+====Reference====
+
+
+* https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/
+
+* https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4698
+
+* https://redcanary.com/threat-detection-report/techniques/scheduled-task-job/
+
+* https://docs.microsoft.com/en-us/windows/win32/taskschd/time-trigger-example--scripting-?redirectedfrom=MSDN
+
+* https://app.any.run/tasks/e26f1b2e-befa-483b-91d2-e18636e2faf3/
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/taskschedule/windows-security.log
+
+
+''version'': 1
+
+
+
+
+====Search====
+`wineventlog_security` EventCode=4698
+| xmlkv Message
+| search Command IN ("*powershell.exe*", "*wscript.exe*", "*cscript.exe*", "*cmd.exe*", "*sh.exe*", "*ksh.exe*", "*zsh.exe*", "*bash.exe*", "*scrcons.exe*", "*pwsh.exe*")
+| stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `winevent_scheduled_task_created_to_spawn_shell_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#Windows_Persistence_Techniques|Windows Persistence Techniques]]
+
+* [[Documentation:ESSOC:stories:UseCase#Ransomware|Ransomware]]
+
+* [[Documentation:ESSOC:stories:UseCase#Ryuk_Ransomware|Ryuk Ransomware]]
+
+
+====How To Implement====
+To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required.
+
+====Required field====
+
+* _time
+
+* dest
+
+* Task_Name
+
+* Description
+
+* Command
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1053.005
+| Scheduled Task
+| Execution, Persistence, Privilege Escalation
+|}
+
+
+====Kill Chain Phase====
+
+* Privilege Escalation
+
+
+====Known False Positives====
+False positives are possible if legitimate applications are allowed to register tasks that call a shell to be spawned. Filter as needed based on command-line or processes that are used legitimately.
+
+====Reference====
+
+
+* https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/
+
+* https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4698
+
+* https://redcanary.com/threat-detection-report/techniques/scheduled-task-job/
+
+* https://docs.microsoft.com/en-us/windows/win32/taskschd/time-trigger-example--scripting-?redirectedfrom=MSDN
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-security.log
+
+
''version'': 1
+
+
+====Search====
+
+| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=winword.exe Processes.process_name=cmd.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+| `drop_dm_object_name(Processes)`
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `winword_spawning_cmd_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#Spearphishing_Attachments|Spearphishing Attachments]]
+
+
+====How To Implement====
+To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
+
+====Required field====
+
+* _time
+
+* process_name
+
+* process_id
+
+* parent_process_name
+
+* dest
+
+* user
+
+* parent_process_id
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1566.001
+| Spearphishing Attachment
+| Initial Access
+|}
+
+
+====Kill Chain Phase====
+
+* Exploitation
+
+
+====Known False Positives====
+False positives should be limited, but if any are present, filter as needed.
+
+====Reference====
+
+
+* https://app.any.run/tasks/73af0064-a785-4c0a-ab0d-cde593fe16ef/
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log
+
+
+''version'': 1
+
+
+
+
+====Search====
+
+| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="winword.exe" Processes.process_name IN ("powershell.exe", "pwsh.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+| `drop_dm_object_name(Processes)`
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `winword_spawning_powershell_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#Spearphishing_Attachments|Spearphishing Attachments]]
+
+
+====How To Implement====
+To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
+
+====Required field====
+
+* _time
+
+* process_name
+
+* process_id
+
+* parent_process_name
+
+* dest
+
+* user
+
+* parent_process_id
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1566.001
+| Spearphishing Attachment
+| Initial Access
+|}
+
+
+====Kill Chain Phase====
+
+* Exploitation
+
+
+====Known False Positives====
+False positives should be limited, but if any are present, filter as needed.
+
+====Reference====
+
+
+* https://redcanary.com/threat-detection-report/techniques/powershell/
+
+* https://attack.mitre.org/techniques/T1566/001/
+
+* https://app.any.run/tasks/b79fa381-f35c-4b3e-8d02-507e7ee7342f/
+
+* https://app.any.run/tasks/181ac90b-0898-4631-8701-b778a30610ad/
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log
+
+
+''version'': 1
+
+
+
+
+====Search====
+
+| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="winword.exe" Processes.process_name IN ("cscript.exe", "wscript.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
+| `drop_dm_object_name(Processes)`
+| `security_content_ctime(firstTime)`
+| `security_content_ctime(lastTime)`
+| `winword_spawning_windows_script_host_filter`
+
+====Associated Analytic Story====
+
+* [[Documentation:ESSOC:stories:UseCase#Spearphishing_Attachment|Spearphishing Attachment]]
+
+
+====How To Implement====
+To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.
+
+====Required field====
+
+* _time
+
+* process_name
+
+* process_id
+
+* parent_process_name
+
+* dest
+
+* user
+
+* parent_process_id
+
+
+
+====ATT&CK====
+{|
+! style="text-align:left;"| ID
+! Technique
+! Tactic
+|-
+| T1566.001
+| Spearphishing Attachment
+| Initial Access
+|}
+
+
+====Kill Chain Phase====
+
+* Exploitation
+
+
+====Known False Positives====
+There will be limited false positives and it will be different for every environment. Tune by child process or command-line as needed.
+
+====Reference====
+
+
+* https://attack.mitre.org/techniques/T1566/001/
+
+
+
+====Test Dataset====
+
+* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_wsh.log
+
+
''version'': 1