diff --git a/contentctl.yml b/contentctl.yml index 62d9287b03..7b46211ee6 100644 --- a/contentctl.yml +++ b/contentctl.yml @@ -92,7 +92,7 @@ apps: version: 1.3.0 description: description of app hardcoded_path: https://attack-range-appbinaries.s3.us-west-2.amazonaws.com/splunk-add-on-for-microsoft-iis_130.tgz -- uid: 4242 +- uid: 6994 title: CCX Add-on for Suricata appid: SPLUNK_TA_FOR_SURICATA version: 1.0.1 @@ -250,6 +250,12 @@ apps: version: 0.1.2 description: description of app hardcoded_path: https://attack-range-appbinaries.s3.us-west-2.amazonaws.com/mcp-ta_012.tgz +- uid: 8574 + title: TA-osquery + appid: ta-osquery + version: 1.0.4 + description: description of app + hardcoded_path: https://attack-range-appbinaries.s3.us-west-2.amazonaws.com/ta-osquery_104.tgz githash: d6fac80e6d50ae06b40f91519a98489d4ce3a3fd test_data_caches: - base_url: https://media.githubusercontent.com/media/splunk/attack_data/master/ diff --git a/data_sources/osquery.yml b/data_sources/osquery_results.yml similarity index 95% rename from data_sources/osquery.yml rename to data_sources/osquery_results.yml index b14df40563..a644252dce 100644 --- a/data_sources/osquery.yml +++ b/data_sources/osquery_results.yml @@ -1,7 +1,7 @@ -name: osquery +name: Osquery Results id: 7ec4d7c8-c1d0-423a-9169-261f6adb74c0 -version: 2 -date: '2025-01-23' +version: 3 +date: '2026-04-13' author: Patrick Bareiss, Splunk description: Logs system queries performed using osquery, including details about processes, file access, network activity, and system configurations. @@ -13,7 +13,10 @@ mitre_components: - Application Log Content source: osquery sourcetype: osquery:results -supported_TA: [] +supported_TA: +- name: TA-osquery + url: https://splunkbase.splunk.com/app/8574 + version: 1.0.4 fields: - _time - calendarTime diff --git a/detections/endpoint/macos_account_created.yml b/detections/endpoint/macos_account_created.yml new file mode 100644 index 0000000000..d51159959d --- /dev/null +++ b/detections/endpoint/macos_account_created.yml @@ -0,0 +1,101 @@ +name: MacOS Account Created +id: 491004ae-694f-453e-b1e0-fc1e65daeea1 +version: 1 +date: '2026-02-26' +author: Raven Tait, Splunk +status: production +type: Anomaly +description: |- + The following analytic detects the creation of a new local user account on a MacOS system. It leverages osquery logs to identify this activity. + Monitoring the creation of local accounts is crucial for a SOC as it can indicate unauthorized access or lateral movement within the network. + If confirmed malicious, this activity could allow an attacker to establish persistence, escalate privileges, or gain unauthorized access to sensitive systems and data. +data_source: + - Osquery Results +search: |- + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + from datamodel=Endpoint.Processes where + + ( + Processes.process = "*sysadminctl" + Processes.process = "*-addUser*" + ) + OR + ( + Processes.process = "*createhomedir*" + Processes.process = "*-u*" + ) + OR + ( + Processes.process = "*dseditgroup*" + Processes.process IN ( + "*edit*", + "*-a*" + ) + ) + OR + ( + Processes.process = "*dscl*" + Processes.process = "*-create*" + ) + + by Processes.dest Processes.original_file_name Processes.parent_process_id + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id + Processes.process_current_directory Processes.process_name + Processes.process_path Processes.user Processes.user_id + Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `macos_account_created_filter` +how_to_implement: |- + This detection uses osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery. + Also the [TA-OSquery](https://splunkbase.splunk.com/app/8574) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the data models. +known_false_positives: |- + Creating new accounts after initial endpoint management should be rare in most environments. Investigate and tune as needed. +references: + - https://osquery.readthedocs.io/en/stable/deployment/process-auditing/ + - https://ss64.com/mac/sysadminctl.html + - https://ss64.com/mac/dseditgroup.html + - https://ss64.com/mac/dscl.html +drilldown_searches: + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +rba: + message: New local account created on $dest$ by $user$ via $process$ + risk_objects: + - field: user + type: user + score: 20 + - field: dest + type: system + score: 20 + threat_objects: + - field: process + type: process +tags: + analytic_story: + - MacOS Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1136 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint +tests: + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136/osquery_account_creation/osquery.log + source: osquery + sourcetype: osquery:results diff --git a/detections/endpoint/macos_amos_stealer___virtual_machine_check_activity.yml b/detections/endpoint/macos_amos_stealer___virtual_machine_check_activity.yml index 95a2d93601..184013301e 100644 --- a/detections/endpoint/macos_amos_stealer___virtual_machine_check_activity.yml +++ b/detections/endpoint/macos_amos_stealer___virtual_machine_check_activity.yml @@ -1,19 +1,27 @@ name: MacOS AMOS Stealer - Virtual Machine Check Activity id: 4e41ad21-9761-426d-8aa1-083712ff9f30 -version: 5 -date: '2026-03-10' +version: 6 +date: '2026-04-13' author: Nasreddine Bencherchali, Splunk, Alex Karkins status: production type: Anomaly description: | - The following analytic detects AMOS Stealer VM check activity on macOS. It leverages osquery to monitor process events and identifies the execution of the "osascript" command along with specific commandline strings. This activity is significant - as AMOS stealer was seen using this pattern in order to check if the host is a Virtual Machine or not. If confirmed malicious, this behavior indicate that the host is already infected by the AMOS stealer, which could allow attackers to execute arbitrary code, escalate privileges, steal information, or persist within the environment, posing a significant security risk. + The following analytic detects AMOS Stealer VM check activity on macOS. It leverages osquery to monitor process events and identifies the execution of the "osascript" command along with specific commandline strings. + This activity is significant as AMOS stealer was seen using this pattern in order to check if the host is a Virtual Machine or not. + If confirmed malicious, this behavior indicate that the host is already infected by the AMOS stealer, which could allow attackers to execute arbitrary code, escalate privileges, steal information, or persist within the environment, posing a significant security risk. data_source: - - osquery + - Osquery Results search: | - `osquery_macro` name=es_process_events - columns.cmdline="*osascript*" AND columns.cmdline="* -e *" AND columns.cmdline="*set*" AND columns.cmdline="*system_profiler*" AND columns.cmdline IN ("*VMware*", "*QEMU*") + `osquery_macro` + name=es_process_events + columns.cmdline="*osascript*" + columns.cmdline="* -e *" + columns.cmdline="*set*" + columns.cmdline="*system_profiler*" + columns.cmdline IN ("*VMware*", "*QEMU*") + | rename columns.* as * + | stats min(_time) as firstTime max(_time) as lastTime values(cmdline) as cmdline, values(pid) as pid, @@ -21,12 +29,14 @@ search: | values(path) as path, values(signing_id) as signing_id, by username host + | rename username as user, cmdline as process, parent as parent_process, path as process_path, host as dest + | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `macos_amos_stealer___virtual_machine_check_activity_filter` diff --git a/detections/endpoint/macos_data_chunking.yml b/detections/endpoint/macos_data_chunking.yml new file mode 100644 index 0000000000..dd1fcc8497 --- /dev/null +++ b/detections/endpoint/macos_data_chunking.yml @@ -0,0 +1,88 @@ +name: MacOS Data Chunking +id: 7f1c8bed-9bd4-40b0-a1df-c262cbade0fc +version: 1 +date: '2026-02-26' +author: Raven Tait, Splunk +status: production +type: Anomaly +description: |- + The following analytic detects suspicious data chunking activities that involve the use of split or dd, potentially indicating an attempt to evade detection by breaking large files into smaller parts. + Attackers may use this technique to bypass size-based security controls, facilitating the covert exfiltration of sensitive data. + By monitoring for unusual or unauthorized use of these commands, this analytic helps identify potential data exfiltration attempts, allowing security teams to intervene and prevent the unauthorized transfer of critical information from the network. +data_source: + - Osquery Results +search: |- + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + + from datamodel=Endpoint.Processes where + + ( + Processes.process = "dd *" + Processes.process = "* if=*" + ) + OR + ( + Processes.process = "*split *" + Processes.process="* -b *" + ) + + by Processes.dest Processes.original_file_name Processes.parent_process_id + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id + Processes.process_current_directory Processes.process_name + Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `macos_data_chunking_filter` +how_to_implement: |- + This detection uses osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery. + Also the [TA-OSquery](https://splunkbase.splunk.com/app/8574) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the data models. +known_false_positives: |- + Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives. +references: + - https://osquery.readthedocs.io/en/stable/deployment/process-auditing/ + - https://ss64.com/mac/dd.html + - https://ss64.com/mac/split.html +drilldown_searches: + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +rba: + message: A file was split on $dest$ by $user$ via $process$ + risk_objects: + - field: user + type: user + score: 20 + - field: dest + type: system + score: 20 + threat_objects: + - field: process + type: process +tags: + analytic_story: + - MacOS Post-Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1030 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint +tests: + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1030/osquery_data_chunking/osquery.log + source: osquery + sourcetype: osquery:results diff --git a/detections/endpoint/macos_gatekeeper_bypass.yml b/detections/endpoint/macos_gatekeeper_bypass.yml new file mode 100644 index 0000000000..f12a1001f8 --- /dev/null +++ b/detections/endpoint/macos_gatekeeper_bypass.yml @@ -0,0 +1,89 @@ +name: MacOS Gatekeeper Bypass +id: 2c9346f3-bbeb-48ce-8411-fc13d09d83a5 +version: 1 +date: '2026-02-26' +author: Raven Tait, Splunk +status: production +type: Anomaly +description: |- + Detects known MacOS security bypass techniques that may be used to enable malicious code execution. + Specifically monitors for attempts to remove the com.apple.quarantine attribute using xattr, or to disable Gatekeeper protections via spctl --master-disable, both of which can allow untrusted or malicious applications to execute without standard system safeguards. +data_source: + - Osquery Results +search: |- + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + + from datamodel=Endpoint.Processes where + + ( + Processes.process = "*xattr*" + Processes.process = "*com.apple.quarantine*" + ) + OR + ( + Processes.process = "*spctl*" + Processes.process = "*master-disable*" + ) + + by Processes.dest Processes.original_file_name Processes.parent_process_id + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id + Processes.process_current_directory Processes.process_name + Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `macos_gatekeeper_bypass_filter` +how_to_implement: |- + This detection uses osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery. + Also the [TA-OSquery](https://splunkbase.splunk.com/app/8574) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the data models. +known_false_positives: |- + Administrators or power users may need to disable Gatekeeper to install unsigned tools. +references: + - https://osquery.readthedocs.io/en/stable/deployment/process-auditing/ + - https://ss64.com/mac/xattr.html + - https://ss64.com/mac/spctl.html +drilldown_searches: + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +rba: + message: Attempt to bypass gatekeeper protections on $dest$ by $user$ via $process$ + risk_objects: + - field: user + type: user + score: 20 + - field: dest + type: system + score: 20 + threat_objects: + - field: process + type: process +tags: + analytic_story: + - MacOS Privilege Escalation + - MacOS Post-Exploitation + - MacOS Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1553.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint +tests: + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.001/osquery_gatekeeper/osquery.log + source: osquery + sourcetype: osquery:results diff --git a/detections/endpoint/macos_hidden_files_and_directories.yml b/detections/endpoint/macos_hidden_files_and_directories.yml new file mode 100644 index 0000000000..04b3eaf05e --- /dev/null +++ b/detections/endpoint/macos_hidden_files_and_directories.yml @@ -0,0 +1,85 @@ +name: MacOS Hidden Files and Directories +id: 51c43b7b-e406-45d2-9bad-5c67f07e6528 +version: 1 +date: '2026-02-26' +author: Raven Tait, Splunk +status: production +type: Anomaly +description: |- + The following analytic detects suspicious creation of hidden files and directories, which may indicate an attacker's attempt to conceal malicious activities or unauthorized data. + Hidden files and directories are often used to evade detection by security tools and administrators, providing a stealthy means for storing malware, logs, or sensitive information. + By monitoring for unusual or unauthorized creation of hidden files and directories, this analytic helps identify potential attempts to hide or unauthorized creation of hidden files and directories, and helps identify potential attempts to hide malicious operations, enabling security teams to uncover and address hidden threats effectively. +data_source: + - Osquery Results +search: |- + | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where + + ( + Processes.process="*chflags *" + Processes.process="* hidden*" + ) + OR + ( + Processes.process="*xattr *" + Processes.process="* -wx *" + Processes.process="*com.apple.FinderInfo*" + ) + + by Processes.dest Processes.original_file_name Processes.parent_process_id + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id + Processes.process_current_directory Processes.process_name + Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `macos_hidden_files_and_directories_filter` +how_to_implement: |- + This detection uses osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery. + Also the [TA-OSquery](https://splunkbase.splunk.com/app/8574) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the data models. +known_false_positives: |- + Power users or developers utilizing build tools or CI/CD tools could trigger this activity. +references: + - https://osquery.readthedocs.io/en/stable/deployment/process-auditing/ + - https://ss64.com/mac/chflags.html + - https://ss64.com/mac/xattr.html +drilldown_searches: + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +rba: + message: Attempt to hide files on $dest$ by $user$ via $process$ + risk_objects: + - field: user + type: user + score: 20 + - field: dest + type: system + score: 20 + threat_objects: + - field: process + type: process +tags: + analytic_story: + - MacOS Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1564.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint +tests: + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1564.001/osquery_hidden_files/osquery.log + source: osquery + sourcetype: osquery:results diff --git a/detections/endpoint/macos_kextload_usage.yml b/detections/endpoint/macos_kextload_usage.yml new file mode 100644 index 0000000000..2f60c09536 --- /dev/null +++ b/detections/endpoint/macos_kextload_usage.yml @@ -0,0 +1,86 @@ +name: MacOS Kextload Usage +id: 9d680775-84a6-4625-a8ea-8182b9427ce4 +version: 1 +date: '2026-02-26' +author: Raven Tait, Splunk +status: production +type: TTP +description: |- + Detects execution of the kextload command on macOS systems. The kextload utility is used to manually load kernel extensions (KEXTs) into the macOS kernel, which can introduce privileged code at the kernel level. + While legitimate for driver installation and system administration, misuse may indicate attempts to install unauthorized, malicious, or persistence-enabling kernel extensions. +data_source: + - Osquery Results +search: |- + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + + from datamodel=Endpoint.Processes where + + Processes.process_name = "kextload" + + AND NOT + + Processes.process IN ( + "*-help*", + "* -h *" + ) + + by Processes.dest Processes.original_file_name Processes.parent_process_id + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id + Processes.process_current_directory Processes.process_name + Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `macos_kextload_usage_filter` +how_to_implement: |- + This detection uses osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery. + Also the [TA-OSquery](https://splunkbase.splunk.com/app/8574) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the data models. +known_false_positives: |- + Administrators installing new drivers could use this application. +references: + - https://osquery.readthedocs.io/en/stable/deployment/process-auditing/ + - https://www.unix.com/man_page/osx/8/kextload/ +drilldown_searches: + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +rba: + message: Possible kernel extension loaded on $dest$ by $user$ via $process$ + risk_objects: + - field: user + type: user + score: 50 + - field: dest + type: system + score: 50 + threat_objects: + - field: process + type: process +tags: + analytic_story: + - MacOS Privilege Escalation + - MacOS Persistence Techniques + asset_type: Endpoint + mitre_attack_id: + - T1543 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint +tests: + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543/osquery_ketxload/osquery.log + source: osquery + sourcetype: osquery:results diff --git a/detections/endpoint/macos_keychains_dumped.yml b/detections/endpoint/macos_keychains_dumped.yml new file mode 100644 index 0000000000..13a94790d8 --- /dev/null +++ b/detections/endpoint/macos_keychains_dumped.yml @@ -0,0 +1,82 @@ +name: MacOS Keychains Dumped +id: dcb45a09-5e6f-441e-b2f8-cbbf923e36d9 +version: 1 +date: '2026-02-24' +author: Raven Tait, Splunk +status: production +type: TTP +description: |- + Detects command-line attempts to access or dump macOS Keychain files. Adversaries may use native utilities or direct file access to extract plaintext credentials from Keychain databases located in ~/Library/Keychains/ or /Library/Keychains/. + This technique is commonly associated with post-exploitation credential harvesting, where an attacker with local access seeks to escalate privileges or move laterally by obtaining stored credentials for applications, Wi-Fi networks, and system services. +data_source: + - Osquery Results +search: |- + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + + from datamodel=Endpoint.Processes where + + Processes.process IN ( + "*dump-keychain -d*", + "*keychaindump*" + ) + + Processes.process="*/library/keychains*" + + by Processes.dest Processes.original_file_name Processes.parent_process_id + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id + Processes.process_current_directory Processes.process_name + Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `macos_keychains_dumped_filter` +how_to_implement: |- + This detection uses osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery. + Also the [TA-OSquery](https://splunkbase.splunk.com/app/8574) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the data models. +known_false_positives: |- + Administrators accessing keychain files for troubleshooting or endpoint management. +references: + - https://osquery.readthedocs.io/en/stable/deployment/process-auditing/ + - https://gist.github.com/hfeeki/88c12f01d00534e09a84 + - https://ss64.com/mac/security-keychain-settings.html +drilldown_searches: + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +rba: + message: Keychains dumped on $dest$ by $user$ via $process$ + risk_objects: + - field: user + type: user + score: 50 + - field: dest + type: system + score: 50 + threat_objects: [] +tags: + analytic_story: + - MacOS Privilege Escalation + asset_type: Endpoint + mitre_attack_id: + - T1555.001 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint +tests: + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555.001/osquery_keychains/osquery.log + source: osquery + sourcetype: osquery:results diff --git a/detections/endpoint/macos_log_removal.yml b/detections/endpoint/macos_log_removal.yml new file mode 100644 index 0000000000..c439fc7248 --- /dev/null +++ b/detections/endpoint/macos_log_removal.yml @@ -0,0 +1,86 @@ +name: MacOS Log Removal +id: a7f2e891-3c4d-4a1b-9e6f-2b8d0c5a1f3e +version: 1 +date: '2026-02-27' +author: Raven Tait, Splunk +status: production +type: TTP +description: |- + Detects the deletion or modification of logs on MacOS systems by identifying execution of the rm command with command-line arguments referencing system.log or audit-related paths. + Adversaries may remove or alter log files to cover their tracks and hinder detection and forensic analysis. This behavior commonly occurs during post-exploitation cleanup. +data_source: + - Osquery Results +search: |- + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + + from datamodel=Endpoint.Processes where + + Processes.process = "*system.log*" + AND + ( + (Processes.process = "*rm *") + OR + ( + Processes.process = "*audit*" + Processes.process = "* -s *" + ) + ) + + by Processes.dest Processes.original_file_name Processes.parent_process_id + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id + Processes.process_current_directory Processes.process_name + Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `macos_log_removal_filter` +how_to_implement: |- + This detection uses osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery. + Also the [TA-OSquery](https://splunkbase.splunk.com/app/8574) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the data models. +known_false_positives: |- + Legitimate log rotation or administrative cleanup of system or audit logs. +references: + - https://osquery.readthedocs.io/en/stable/deployment/process-auditing/ +drilldown_searches: + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +rba: + message: Log removal or modification on $dest$ by $user$ + risk_objects: + - field: user + type: user + score: 55 + - field: dest + type: system + score: 55 + threat_objects: + - field: process + type: process +tags: + analytic_story: + - MacOS Post-Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1070 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint +tests: + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/osquery_log_removal/osquery.log + source: osquery + sourcetype: osquery:results diff --git a/detections/endpoint/macos_loginhook_persistence.yml b/detections/endpoint/macos_loginhook_persistence.yml new file mode 100644 index 0000000000..18e9679136 --- /dev/null +++ b/detections/endpoint/macos_loginhook_persistence.yml @@ -0,0 +1,81 @@ +name: MacOS LoginHook Persistence +id: a04832e7-9d1d-49b1-a684-e31bcd775c77 +version: 1 +date: '2026-02-27' +author: Raven Tait, Splunk +status: production +type: TTP +description: |- + Identifies attempts to configure a macOS LoginHook via the defaults utility. LoginHooks enable automatic execution of a script or program upon user login and have historically been abused for persistence. + Creation or modification of this setting may indicate an attempt to establish startup execution outside standard LaunchAgent mechanisms. +data_source: + - Osquery Results +search: |- + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + + from datamodel=Endpoint.Processes where + + Processes.process = "*defaults *" + Processes.process = "*write*" + Processes.process = "*loginwindow*" + Processes.process = "*loginhook*" + + by Processes.dest Processes.original_file_name Processes.parent_process_id + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id + Processes.process_current_directory Processes.process_name + Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `macos_loginhook_persistence_filter` +how_to_implement: |- + This detection uses osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery. + Also the [TA-OSquery](https://splunkbase.splunk.com/app/8574) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the data models. +known_false_positives: |- + This method is possibly still used by legacy enterprise management scripts. Update filter macro to remove false positives. +references: + - https://osquery.readthedocs.io/en/stable/deployment/process-auditing/ + - https://www.loobins.io/binaries/defaults/ +drilldown_searches: + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +rba: + message: Loginhook created on $dest$ by $user$ via $process$ + risk_objects: + - field: user + type: user + score: 50 + - field: dest + type: system + score: 50 + threat_objects: + - field: process + type: process +tags: + analytic_story: + - MacOS Post-Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1037.002 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint +tests: + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1037.002/osquery_logon_scripts/osquery.log + source: osquery + sourcetype: osquery:results diff --git a/detections/endpoint/macos_lolbin.yml b/detections/endpoint/macos_lolbin.yml index 1d873c15b8..410c7885de 100644 --- a/detections/endpoint/macos_lolbin.yml +++ b/detections/endpoint/macos_lolbin.yml @@ -1,25 +1,54 @@ name: MacOS LOLbin id: 58d270fb-5b39-418e-a855-4b8ac046805e version: 12 -date: '2026-03-31' +date: '2026-04-13' author: Patrick Bareiss, Splunk status: production type: TTP -description: The following analytic detects multiple executions of Living off the Land (LOLbin) binaries on macOS within a short period. It leverages osquery to monitor process events and identifies commands such as "find", "crontab", "screencapture", "openssl", "curl", "wget", "killall", and "funzip". This activity is significant as LOLbins are often used by attackers to perform malicious actions while evading detection. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or persist within the environment, posing a significant security risk. +description: |- + The following analytic detects multiple executions of Living off the Land (LOLbin) binaries on macOS within a short period. + It leverages osquery to monitor process events and identifies commands such as "find", "crontab", "screencapture", "openssl", "curl", "wget", "killall", and "funzip". This activity is significant as LOLbins are often used by attackers to perform malicious actions while evading detection. + If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or persist within the environment, posing a significant security risk. data_source: - - osquery + - Osquery Results search: |- - `osquery_macro` name=es_process_events columns.cmdline IN ("find*", "crontab*", "screencapture*", "openssl*", "curl*", "wget*", "killall*", "funzip*", "chmod*") - | rename columns.* as * - | stats min(_time) as firstTime max(_time) as lastTime values(cmdline) as cmdline, values(pid) as pid, values(parent) as parent, values(path) as path, values(signing_id) as signing_id, dc(path) as dc_path + `osquery_macro` + name=es_process_events + columns.cmdline IN ( + "chmod*", + "crontab*", + "curl*", + "find*", + "funzip*", + "killall*", + "openssl*", + "screencapture*", + "wget*", + ) + | rename columns.* as * + | stats count min(_time) as firstTime + max(_time) as lastTime + values(cmdline) as cmdline + values(pid) as pid + values(parent) as parent + values(path) as path + values(signing_id) as signing_id + dc(path) as dc_path BY username host - | rename username as user, cmdline as process, path as process_path, host as dest - | where dc_path > 3 - | `security_content_ctime(firstTime)` - | `security_content_ctime(lastTime)` - | `macos_lolbin_filter` -how_to_implement: This detection uses osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery. -known_false_positives: No false positives have been identified at this time. + + | rename username as user + cmdline as process + path as process_path + host as dest + + | where dc_path > 3 + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `macos_lolbin_filter` +how_to_implement: |- + This detection uses osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery. +known_false_positives: |- + No false positives have been identified at this time. references: - https://osquery.readthedocs.io/en/stable/deployment/process-auditing/ drilldown_searches: diff --git a/detections/endpoint/macos_network_share_discovery.yml b/detections/endpoint/macos_network_share_discovery.yml new file mode 100644 index 0000000000..64ae1383d5 --- /dev/null +++ b/detections/endpoint/macos_network_share_discovery.yml @@ -0,0 +1,78 @@ +name: MacOS Network Share Discovery +id: a5f5fe52-8e50-4fb0-ad1b-780be6c0d857 +version: 1 +date: '2026-03-02' +author: Raven Tait, Splunk +status: production +type: Anomaly +description: |- + Identifies execution of network share enumeration commands (smbutil, showmount) that can be leveraged by adversaries to discover accessible SMB and NFS resources, supporting internal reconnaissance and potential lateral movement. +data_source: + - Osquery Results +search: |- + | tstats `security_content_summariesonly` + count min(_time) as firstTime + max(_time) as lastTime + + from datamodel=Endpoint.Processes where + + Processes.process IN ("*showmount *", "*smbutil *") + + by Processes.dest Processes.original_file_name Processes.parent_process_id + Processes.process Processes.process_exec Processes.process_guid + Processes.process_hash Processes.process_id + Processes.process_current_directory Processes.process_name + Processes.process_path Processes.user + Processes.user_id Processes.vendor_product + + | `drop_dm_object_name(Processes)` + | `security_content_ctime(firstTime)` + | `security_content_ctime(lastTime)` + | `macos_network_share_discovery_filter` +how_to_implement: |- + This detection uses osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery. + Also the [TA-OSquery](https://splunkbase.splunk.com/app/8574) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the data models. +known_false_positives: |- + Administrators may utilize these tools occasionaly for troubleshooting. +references: + - https://osquery.readthedocs.io/en/stable/deployment/process-auditing/ + - https://leopard-adc.pepas.com/documentation/Darwin/Reference/ManPages/man8/showmount.8.html + - https://www.unix.com/man_page/osx/1/smbutil/ +drilldown_searches: + - name: View the detection results for - "$user$" and "$dest$" + search: '%original_detection_search% | search user = "$user$" dest = "$dest$"' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ + - name: View risk events for the last 7 days for - "$user$" and "$dest$" + search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") starthoursago=168 | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' + earliest_offset: $info_min_time$ + latest_offset: $info_max_time$ +rba: + message: Network share information enumerated on $dest$ by $user$ via $process$ + risk_objects: + - field: user + type: user + score: 20 + - field: dest + type: system + score: 20 + threat_objects: + - field: process + type: process +tags: + analytic_story: + - MacOS Post-Exploitation + asset_type: Endpoint + mitre_attack_id: + - T1135 + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + security_domain: endpoint +tests: + - name: True Positive Test + attack_data: + - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1135/osquery_share_discovery/osquery.log + source: osquery + sourcetype: osquery:results diff --git a/detections/endpoint/macos_plutil.yml b/detections/endpoint/macos_plutil.yml index bd8ed7ba39..26bfc902a3 100644 --- a/detections/endpoint/macos_plutil.yml +++ b/detections/endpoint/macos_plutil.yml @@ -1,13 +1,13 @@ name: MacOS plutil id: c11f2b57-92c1-4cd2-b46c-064eafb833ac -version: 9 -date: '2026-03-10' +version: 10 +date: '2026-04-13' author: Patrick Bareiss, Splunk status: production type: TTP description: The following analytic detects the usage of the `plutil` command to modify plist files on macOS systems. It leverages osquery to monitor process events, specifically looking for executions of `/usr/bin/plutil`. This activity is significant because adversaries can use `plutil` to alter plist files, potentially adding malicious binaries or command-line arguments that execute upon user logon or system startup. If confirmed malicious, this could allow attackers to achieve persistence, execute arbitrary code, or escalate privileges, posing a significant threat to the system's security. data_source: - - osquery + - Osquery Results search: |- `osquery_macro` name=es_process_events columns.path=/usr/bin/plutil | rename columns.* as * diff --git a/detections/endpoint/processes_tapping_keyboard_events.yml b/detections/endpoint/processes_tapping_keyboard_events.yml index 4284d32364..f09d8861bc 100644 --- a/detections/endpoint/processes_tapping_keyboard_events.yml +++ b/detections/endpoint/processes_tapping_keyboard_events.yml @@ -1,13 +1,13 @@ name: Processes Tapping Keyboard Events id: 2a371608-331d-4034-ae2c-21dda8f1d0ec -version: 10 -date: '2026-03-10' +version: 11 +date: '2026-04-13' author: Jose Hernandez, Splunk status: experimental type: TTP description: The following analytic detects processes on macOS systems that are tapping keyboard events, potentially monitoring all keystrokes made by a user. It leverages data from osquery results within the Alerts data model, focusing on specific process names and command lines. This activity is significant as it is a common technique used by Remote Access Trojans (RATs) to log keystrokes, posing a serious security risk. If confirmed malicious, this could lead to unauthorized access to sensitive information, including passwords and personal data, compromising the integrity and confidentiality of the system. data_source: - - osquery + - Osquery Results search: |- | from datamodel Alerts.Alerts | search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps diff --git a/detections/endpoint/suspicious_plistbuddy_usage_via_osquery.yml b/detections/endpoint/suspicious_plistbuddy_usage_via_osquery.yml index f92630fb73..1ac24de917 100644 --- a/detections/endpoint/suspicious_plistbuddy_usage_via_osquery.yml +++ b/detections/endpoint/suspicious_plistbuddy_usage_via_osquery.yml @@ -1,13 +1,13 @@ name: Suspicious PlistBuddy Usage via OSquery id: 20ba6c32-c733-4a32-b64e-2688cf231399 -version: 10 -date: '2026-03-10' +version: 11 +date: '2026-04-13' author: Michael Haag, Splunk status: experimental type: TTP description: The following analytic detects the use of the PlistBuddy utility on macOS to create or modify property list (.plist) files. It leverages OSQuery to monitor process events, specifically looking for commands that interact with LaunchAgents and set properties like RunAtLoad. This activity is significant because PlistBuddy can be used to establish persistence mechanisms, as seen in malware like Silver Sparrow. If confirmed malicious, this could allow an attacker to maintain persistence, execute arbitrary commands, and potentially escalate privileges on the compromised system. data_source: - - osquery + - Osquery Results search: |- `osquery_process` "columns.cmdline"="*LaunchAgents*" OR "columns.cmdline"="*RunAtLoad*" OR "columns.cmdline"="*true*" | `suspicious_plistbuddy_usage_via_osquery_filter` diff --git a/stories/macos_persistence_techniques.yml b/stories/macos_persistence_techniques.yml new file mode 100644 index 0000000000..ea0e4464b9 --- /dev/null +++ b/stories/macos_persistence_techniques.yml @@ -0,0 +1,24 @@ +name: MacOS Persistence Techniques +id: 3fc4619d-4a13-45f8-95a2-51056e221a1c +version: 1 +status: production +date: '2026-02-26' +author: Raven Tait, Splunk +description: Monitor for activities and techniques associated with maintaining persistence + on a MacOS system--a sign that an adversary may have compromised your environment. +narrative: Maintaining persistence is one of the first steps taken by attackers after + the initial compromise. Attackers leverage various custom and built-in tools to + ensure survivability and persistent access within a compromised enterprise. This + Analytic Story provides searches to help you identify various behaviors used by + attackers to maintain persistent access to a MacOS environment. +references: +- https://attack.mitre.org/techniques/T1053/ +- https://www.loobins.io/binaries/defaults/ +tags: + category: + - Adversary Tactics + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + usecase: Advanced Threat Detection \ No newline at end of file diff --git a/stories/macos_post_exploitation.yml b/stories/macos_post_exploitation.yml new file mode 100644 index 0000000000..a50d6e05d8 --- /dev/null +++ b/stories/macos_post_exploitation.yml @@ -0,0 +1,22 @@ +name: MacOS Post-Exploitation +id: bae14f9c-929d-4e2b-8fe7-e4680e0edbbb +version: 1 +status: production +date: '2026-02-26' +author: Raven Tait, Splunk +description: This analytic story identifies popular MacOS post exploitation tools such as MacPEAS, MacShellSwift, EvilOSX, chainbreaker, etc +narrative: These tools allow operators find possible exploits or paths for privilege escalation based on stored credentials, user permissions, kernel version and distro version. +references: +- https://attack.mitre.org/matrices/enterprise/macos/ +- https://github.com/UnsaltedHash42/macPEAS +- https://github.com/cedowens/MacShellSwift/tree/master/MacShellSwift +- https://github.com/Marten4n6/EvilOSX +- https://github.com/n0fate/chainbreaker +tags: + category: + - Adversary Tactics + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + usecase: Advanced Threat Detection \ No newline at end of file diff --git a/stories/macos_privilege_escalation.yml b/stories/macos_privilege_escalation.yml new file mode 100644 index 0000000000..e798356557 --- /dev/null +++ b/stories/macos_privilege_escalation.yml @@ -0,0 +1,27 @@ +name: MacOS Privilege Escalation +id: 67f1ebd1-7a3c-4e9b-bb74-9656425db3c4 +version: 1 +status: production +date: '2026-02-26' +author: Raven Tait, Splunk +description: Monitor for and investigate activities that may be associated with a + MacOS privilege-escalation attack, including unusual processes running on endpoints, + schedule task, services, setuid, root execution and more. +narrative: 'Privilege escalation is a "land-and-expand" technique, wherein an adversary + gains an initial foothold on a host and then exploits its weaknesses to increase + his privileges. The motivation is simple: certain actions on a MacOS machine--such + as installing software--may require higher-level privileges than those the attacker + initially acquired. By increasing his privilege level, the attacker can gain the + control required to carry out his malicious ends. This Analytic Story provides searches + to detect and investigate behaviors that attackers may use to elevate their privileges + in your environment.' +references: +- https://attack.mitre.org/tactics/TA0004/ +tags: + category: + - Adversary Tactics + product: + - Splunk Enterprise + - Splunk Enterprise Security + - Splunk Cloud + usecase: Advanced Threat Detection