Merge branch 'main' into new-rule-user-agents

Author: terrancedejesus

detection_rules/etc/integration-manifests.json.gz

@@ -145,7 +145,10 @@
     "kibana.alert.rule.threat.tactic.id": "keyword",
     "kibana.alert.workflow_status": "keyword",
     "kibana.alert.rule.rule_id": "keyword",
-    "kibana.alert.rule.name": "keyword"
+    "kibana.alert.rule.name": "keyword", 
+    "kibana.alert.risk_score": "long",
+    "kibana.alert.rule.type": "keyword",
+    "kibana.alert.rule.threat.tactic.name": "keyword"
   },
   "logs-google_workspace*": {
     "gsuite.admin": "keyword",

detection_rules/etc/integration-schemas.json.gz

@@ -1,6 +1,6 @@
 [project]
 name = "detection_rules"
-version = "1.5.14"
+version = "1.5.15"
 description = "Detection Rules is the home for rules used by Elastic Security. This repository is used for the development, maintenance, testing, validation, and release of rules for Elastic Security’s Detection Engine."
 readme = "README.md"
 requires-python = ">=3.12"

detection_rules/etc/non-ecs-schema.json

@@ -0,0 +1,97 @@
+[metadata]
+creation_date = "2025/11/19"
+maturity = "production"
+updated_date = "2025/11/19"
+
+[rule]
+author = ["Elastic"]
+description = """
+This rule uses alert data to determine when multiple alerts from Elastic Defend involving the same host are triggered.
+Analysts can use this to prioritize triage and response, as these hosts are more likely to be compromised.
+"""
+from = "now-60m"
+interval = "30m"
+language = "esql"
+license = "Elastic License v2"
+name = "Multiple Elastic Defend Alerts by Agent"
+risk_score = 73
+rule_id = "ab25369e-ea5e-46f1-9cd5-478a0a4a131a"
+severity = "high"
+tags = ["Use Case: Threat Detection", "Rule Type: Higher-Order Rule", "Resources: Investigation Guide", "Data Source: Elastic Defend"]
+timestamp_override = "event.ingested"
+type = "esql"
+
+query = '''
+from logs-endpoint.alerts-* metadata _id
+| eval target_time_window = DATE_TRUNC(24 hours, @timestamp)
+| where event.code in ("malicious_file", "memory_signature", "shellcode_thread", "behavior") and
+        agent.id is not null and not rule.name in ("Multi.EICAR.Not-a-virus")
+| stats Esql.alerts_count = COUNT(*),
+        Esql.event_code_distinct_count = count_distinct(event.code),
+        Esql.rule_name_distinct_count = COUNT_DISTINCT(rule.name),
+        Esql.file_hash_distinct_count = COUNT_DISTINCT(file.hash.sha256),
+        Esql.process_name_distinct_count = COUNT_DISTINCT(process.entity_id),
+        Esql.event_code_values = VALUES(event.code),
+        Esql.rule_name_values = VALUES(rule.name),
+        Esql.message_values = VALUES(message),
+        Esql.file_path_values = VALUES(file.path),
+        Esql.dll_path_values = VALUES(dll.path),
+        Esql.process_executable_values = VALUES(process.executable),
+        Esql.process_parent_executable_values = VALUES(process.parent.executable),
+        Esql.process_command_line_values = VALUES(process.command_line),
+        Esql.process_hash_sha256_values = VALUES(process.hash.sha256),
+        Esql.file_hash_sha256_values = VALUES(file.hash.sha256),
+        Esql.dll_hash_sha256_values = VALUES(dll.hash.sha256) by agent.id
+| where (Esql.event_code_distinct_count >= 2 or Esql.rule_name_distinct_count >= 3 or Esql.file_hash_distinct_count >= 2)
+| keep agent.id,
+       Esql.alerts_count,
+       Esql.event_code_distinct_count,
+       Esql.rule_name_distinct_count,
+       Esql.message_values,
+       Esql.event_code_values,
+       Esql.rule_name_values,
+       Esql.process_executable_values,
+       Esql.process_parent_executable_values,
+       Esql.process_command_line_values,
+       Esql.file_path_values,
+       Esql.dll_path_values,
+       Esql.process_hash_sha256_values,
+       Esql.file_hash_sha256_values,
+       Esql.dll_hash_sha256_values
+'''
+note = """## Triage and analysis
+
+> **Disclaimer**:
+> This investigation guide was created using generative AI technology and has been reviewed to improve its accuracy and relevance. While every effort has been made to ensure its quality, we recommend validating the content and adapting it to suit your specific environment and operational needs.
+
+### Investigating Multiple Elastic Defend Alerts by Agent
+
+Endpoint security technologies monitor and analyze activities on devices to detect malicious behavior. Adversaries exploit these systems by deploying malware that triggers specific signatures across multiple hosts, indicating a coordinated attack. The detection rule identifies such threats by analyzing alert data for specific malware signatures across several hosts, flagging potential widespread infections for prioritized investigation.
+
+### Possible investigation steps
+
+- Review the alert details to identify the specific host involved and the different ATT&CK tactics that triggered the alerts.
+- Examine the timeline of the alerts to understand the sequence of events and determine if there is a pattern or progression in the tactics used.
+- Correlate the alert data with other logs and telemetry from the host, such as process creation, network connections, and file modifications, to gather additional context.
+- Investigate any known vulnerabilities or misconfigurations on the host that could have been exploited by the adversary.
+- Check for any indicators of compromise (IOCs) associated with the alerts, such as suspicious IP addresses, domains, or file hashes, and search for these across the network.
+- Assess the impact and scope of the potential compromise by determining if other hosts or systems have similar alerts or related activity.
+
+### False positive analysis
+
+- Alerts from routine administrative tasks may trigger multiple tactics. Review and exclude known benign activities such as scheduled software updates or system maintenance.
+- Security tools running on the host might generate alerts across different tactics. Identify and exclude alerts from trusted security applications to reduce noise.
+- Automated scripts or batch processes can mimic adversarial behavior. Analyze and whitelist these processes if they are verified as non-threatening.
+- Frequent alerts from development or testing environments can be misleading. Consider excluding these environments from the rule or applying a different risk score.
+- User behavior anomalies, such as accessing multiple systems or applications, might trigger alerts. Implement user behavior baselines to differentiate between normal and suspicious activities.
+
+### Response and remediation
+
+- Isolate the affected host from the network immediately to prevent further lateral movement by the adversary.
+- Conduct a thorough forensic analysis of the host to identify the specific vulnerabilities exploited and gather evidence of the attack phases involved.
+- Remove any identified malicious software or unauthorized access tools from the host, ensuring all persistence mechanisms are eradicated.
+- Apply security patches and updates to the host to address any exploited vulnerabilities and prevent similar attacks.
+- Restore the host from a known good backup if necessary, ensuring that the backup is free from compromise.
+- Monitor the host and network for any signs of re-infection or further suspicious activity, using enhanced logging and alerting based on the identified attack patterns.
+- Escalate the incident to the appropriate internal or external cybersecurity teams for further investigation and potential legal action if the attack is part of a larger campaign."""
+references = ["https://github.com/elastic/protections-artifacts/tree/main/yara/rules"]

pyproject.toml

@@ -0,0 +1,105 @@
+[metadata]
+creation_date = "2025/11/18"
+integration = ["endpoint", "panw", "fortinet_fortigate", "suricata"]
+maturity = "production"
+updated_date = "2025/11/18"
+
+[rule]
+author = ["Elastic"]
+description = """
+This rule correlate any Elastic Defend alert with a set of suspicious events from Network security devices like Palo Alto
+Networks (PANW) and Fortinet Fortigate by host.ip and source.ip. This may indicate that this host is compromised and
+triggering multi-datasource alerts.
+"""
+from = "now-60m"
+interval = "10m"
+language = "esql"
+license = "Elastic License v2"
+name = "Elastic Defend and Network Security Alerts Correlation"
+risk_score = 73
+rule_id = "0bca7e73-e1b5-4fb2-801b-9b5f5be20dfe"
+severity = "high"
+tags = [
+    "Use Case: Threat Detection",
+    "Rule Type: Higher-Order Rule",
+    "Resources: Investigation Guide",
+    "Data Source: Elastic Defend",
+    "Data Source: Fortinet",
+    "Data Source: PAN-OS"
+]
+timestamp_override = "event.ingested"
+type = "esql"
+
+query = '''
+FROM logs-* metadata _id
+| WHERE
+        // Elastic Defend Alerts
+        (event.module == "endpoint" and event.dataset == "endpoint.alerts") or
+
+        // PANW suspicious events
+        (event.dataset == "panw.panos" and
+         event.action in ("virus_detected", "wildfire_virus_detected", "c2_communication", "spyware_detected", "large_upload", "denied", "exploit_detected")) or
+
+        // Fortigate suspicious events
+        (event.dataset == "fortinet_fortigate.log" and
+         (event.action in ("outbreak-prevention", "deny", "infected", "blocked") or message like "backdoor*" or message like "Proxy*" or message like "anomaly*" or message like "P2P*" or message like "misc*" or message like "DNS.Over.HTTPS" or message like "Remote.Access")) or
+
+        // Suricata
+        (event.dataset == "suricata.eve" and message in ("Command and Control Traffic", "Potentially Bad Traffic", "A Network Trojan was detected", "Detection of a Network Scan", "Domain Observed Used for C2 Detected", "Malware Command and Control Activity Detected"))
+
+// extract source.ip from PANW or Fortigate events and host.ip from Elastic Defend alert
+|eval fw_alert_source_ip = CASE(event.dataset in ("panw.panos", "fortinet_fortigate.log"), source.ip, null),
+      elastic_defend_alert_host_ip = CASE(event.module == "endpoint" and event.dataset == "endpoint.alerts", host.ip, null)
+| eval Esql.source_ip = COALESCE(fw_alert_source_ip, elastic_defend_alert_host_ip)
+| where Esql.source_ip is not null
+
+// group by host_source_ip shared between FG/PANW and Elastic Defend
+| stats Esql.alerts_count = COUNT(*),
+        Esql.event_module_distinct_count = COUNT_DISTINCT(event.module),
+        Esql.event_module_values = VALUES(event.module),
+        Esql.message_values = VALUES(message),
+        Esql.event_action_values = VALUES(event.action),
+        Esql.process_executable_values = VALUES(process.executable),
+        Esql.host_id_values = VALUES(host.id),
+        Esql.user_name_values = VALUES(user.name),
+        Esql.destination_ip_values = VALUES(destination.ip)
+        by Esql.source_ip
+| where Esql.event_module_distinct_count >= 2
+| keep Esql.alerts_count, Esql.source_ip, Esql.destination_ip_values, Esql.host_id_values, Esql.user_name_values, Esql.event_module_values, Esql.message_values, Esql.process_executable_values
+'''
+note = """## Triage and analysis
+
+### Investigating Elastic Defend and Network Security Alerts Correlation
+
+This rule correlate any Elastic Defend alert with suspicious events from Network Security datasources like Palo Alto Networks (PANW), Fortinet Fortigate and Suricata by host.ip and source.ip.
+
+### Possible investigation steps
+
+- Review the alert details to identify the specific host and users involved.
+- Investiguate the network alerts by destination.ip and message.
+- Examine the timeline of the alerts to understand the sequence of events and determine if there is a pattern or progression in the tactics used.
+- Correlate the alert data with other logs and telemetry from the host, such as process creation, network connections, and file modifications, to gather additional context.
+- Check for any indicators of compromise (IOCs) associated with the alerts, such as suspicious IP addresses, domains, or file hashes, and search for these across the network.
+- Assess the impact and scope of the potential compromise by determining if other hosts or systems have similar alerts or related activity.
+
+### False positive analysis
+
+- IP address ranges overlap where the host.ip value from the Elastic Defend alert is unrelated to the source.ip value from the Network Security alert.
+- Alerts from routine administrative tasks may trigger multiple alerts. Review and exclude known benign activities such as scheduled software updates or system maintenance.
+- Security tools running on the host might generate alerts across different tactics. Identify and exclude alerts from trusted security applications to reduce noise.
+- Automated scripts or batch processes can mimic adversarial behavior. Analyze and whitelist these processes if they are verified as non-threatening.
+- Frequent alerts from development or testing environments can be misleading. Consider excluding these environments from the rule or applying a different risk score.
+- User behavior anomalies, such as accessing multiple systems or applications, might trigger alerts. Implement user behavior baselines to differentiate between normal and suspicious activities.
+
+### Response and remediation
+
+- Isolate the affected host from the network immediately to prevent further lateral movement by the adversary.
+- Conduct a thorough forensic analysis of the host to identify the specific vulnerabilities exploited and gather evidence of the attack phases involved.
+- Remove any identified malicious software or unauthorized access tools from the host, ensuring all persistence mechanisms are eradicated.
+- Apply security patches and updates to the host to address any exploited vulnerabilities and prevent similar attacks.
+- Restore the host from a known good backup if necessary, ensuring that the backup is free from compromise.
+- Monitor the host and network for any signs of re-infection or further suspicious activity, using enhanced logging and alerting based on the identified attack patterns.
+- Escalate the incident to the appropriate internal or external cybersecurity teams for further investigation and potential legal action if the attack is part of a larger campaign."""
+
+
+

rules/cross-platform/multiple_alerts_edr_elastic_defend_by_host.toml

@@ -0,0 +1,82 @@
+[metadata]
+creation_date = "2025/11/19"
+maturity = "production"
+updated_date = "2025/11/19"
+
+[rule]
+author = ["Elastic"]
+description = """
+This rule correlates any Elastic Defend alert with an email security related alert by target user name. This may indicate
+the successful execution of a phishing attack.
+"""
+from = "now-1h"
+interval = "45m"
+language = "esql"
+license = "Elastic License v2"
+name = "Elastic Defend and Email Alerts Correlation"
+risk_score = 73
+rule_id = "c562a800-cf97-464e-9d6f-84db91e86e10"
+severity = "high"
+tags = [
+    "Use Case: Threat Detection",
+    "Rule Type: Higher-Order Rule",
+    "Resources: Investigation Guide",
+    "Data Source: Elastic Defend",
+    "Domain: Email",
+    "Domain: Endpoint"
+]
+timestamp_override = "event.ingested"
+type = "esql"
+
+query = '''
+from logs-* metadata _id
+// Email or Elastic Defend alerts where user name is populated
+| where
+  (event.category == "email" and event.kind == "alert" and destination.user.name is not null) or
+  (event.module == "endpoint" and event.dataset == "endpoint.alerts" and user.name is not null)
+
+// extract target user name from email and endpoint alerts
+| eval email_alert_target_user_name = CASE(event.category == "email", destination.user.name, null),
+       elastic_defend_alert_user_name = CASE(event.module == "endpoint" and event.dataset == "endpoint.alerts", user.name, null)
+| eval Esql.target_user_name = COALESCE(email_alert_target_user_name, elastic_defend_alert_user_name)
+| where Esql.target_user_name is not null
+
+// group by Esql.target_user_name
+| stats Esql.alerts_count = COUNT(*),
+        Esql.event_module_distinct_count = COUNT_DISTINCT(event.module),
+        Esql.event_module_values = VALUES(event.module),
+        Esql.message_values = VALUES(message),
+        Esql.event_action_values = VALUES(event.action),
+        Esql.process_executable_values = VALUES(process.executable),
+        Esql.host_id_values = VALUES(host.id),
+        Esql.source_user_name = VALUES(source.user.name),
+        Esql.rule_name_values = VALUES(rule.name)
+        by Esql.target_user_name
+// alert when same user is observed in an endpoint and email alert
+| where Esql.event_module_distinct_count >= 2
+| keep Esql.alerts_count, Esql.event_module_values, Esql.host_id_values, Esql.source_user_name, Esql.target_user_name, Esql.message_values, Esql.rule_name_values, Esql.event_action_values
+'''
+note = """## Triage and analysis
+### Investigating Elastic Defend and Email Alerts Correlation
+
+This rule correlates any Elastic Defend alert with an email security related alert by target user name.
+
+### Possible investigation steps
+- Review the alert details to identify the specific host and users involved.
+- Investigate the individual alerts for the target user name and see if they are related.
+- Review all emails received from Esql.source_user_name and if there are other impacted users.
+- Correlate the alert data with other logs and telemetry from the host, such as process creation, network connections, and file modifications, to gather additional context.
+- Assess the impact and scope of the potential compromise by determining if other hosts or systems have similar alerts or related activity.
+
+### False positive analysis
+- Legitimate email marked as suspicious.
+- Legitimate file or behavior marked as suspicious by Elastic Defend.
+- Unrelated alerts where the target user name is too generic.
+
+### Response and remediation
+- Isolate the affected host from the network immediately to prevent further lateral movement by the adversary.
+- Conduct a thorough forensic analysis of the host.
+- Remove any identified malicious software or unauthorized access tools from the host, ensuring all persistence mechanisms are eradicated.
+- Restore the host from a known good backup if necessary, ensuring that the backup is free from compromise.
+- Monitor the host and network for any signs of re-infection or further suspicious activity, using enhanced logging and alerting based on the identified attack patterns.
+- Escalate the incident to the appropriate internal or external cybersecurity teams for further investigation and potential legal action if the attack is part of a larger campaign."""