Create multiple_alerts_email_elastic_defend_correlation.toml

Author: Samirbous

rules/cross-platform/multiple_alerts_email_elastic_defend_correlation.toml

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+[metadata]
+creation_date = "2025/11/19"
+maturity = "production"
+updated_date = "2025/11/19"
+
+[rule]
+author = ["Elastic"]
+description = """
+This rule correlate 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",
+    "Use Case: Email Security",
+]
+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 NG-Firewall Alerts Correlation
+This rule correlate any Elastic Defend alert with suspicious events from Next-Gen Firewall like Palo Alto Networks (PANW) and Fortinet Fortigate by host.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
+- 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."""