b39cfc34e6
* [New] First Time Seen Elastic Defend Behavior Alert This rule detects Elastic Defend behavior alerts that are observed for the first time today when compared against the previous 7 days of alert history. It highlights low-volume, newly observed alerts tied to a specific detection rule on a single agent, which may indicate early-stage malicious activity or initial execution of suspicious behavior : * Update first_time_seen_elastic_defend_alert.toml * ++ * Update first_time_seen_elastic_defend_alert.toml * ++ * Update fist_time_seen_elastic_detection_rule.toml * Update fist_time_seen_elastic_detection_rule.toml * Update rules/cross-platform/first_time_seen_elastic_defend_alert.toml Co-authored-by: Mika Ayenson, PhD <Mikaayenson@users.noreply.github.com> * Update rules/cross-platform/fist_time_seen_elastic_detection_rule.toml Co-authored-by: Mika Ayenson, PhD <Mikaayenson@users.noreply.github.com> * Update rules/cross-platform/fist_time_seen_elastic_detection_rule.toml Co-authored-by: Mika Ayenson, PhD <Mikaayenson@users.noreply.github.com> * Update fist_time_seen_elastic_detection_rule.toml * Update first_time_seen_elastic_defend_alert.toml * Update rules/cross-platform/first_time_seen_elastic_defend_alert.toml Co-authored-by: Jonhnathan <26856693+w0rk3r@users.noreply.github.com> * Update rules/cross-platform/first_time_seen_elastic_defend_alert.toml Co-authored-by: Jonhnathan <26856693+w0rk3r@users.noreply.github.com> * Update rules/cross-platform/fist_time_seen_elastic_detection_rule.toml Co-authored-by: Jonhnathan <26856693+w0rk3r@users.noreply.github.com> * Update first_time_seen_elastic_defend_alert.toml * Update and rename fist_time_seen_elastic_detection_rule.toml to newly_observed_elastic_detection_rule.toml * Rename first_time_seen_elastic_defend_alert.toml to newly_observed_elastic_defend_alert.toml * Update newly_observed_elastic_defend_alert.toml * Update newly_observed_elastic_detection_rule.toml * Update newly_observed_elastic_defend_alert.toml * Update newly_observed_elastic_detection_rule.toml * Update newly_observed_elastic_defend_alert.toml * Update newly_observed_elastic_detection_rule.toml * Update newly_observed_elastic_detection_rule.toml --------- Co-authored-by: Mika Ayenson, PhD <Mikaayenson@users.noreply.github.com> Co-authored-by: Jonhnathan <26856693+w0rk3r@users.noreply.github.com>
85 lines
4.3 KiB
TOML
85 lines
4.3 KiB
TOML
[metadata]
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creation_date = "2026/01/07"
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maturity = "production"
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updated_date = "2026/01/07"
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[rule]
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author = ["Elastic"]
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description = """
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This rule detects Elastic SIEM high severity detection alerts that are observed for the first time in the previous 5 days
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of alert history. It highlights low-volume, newly observed alerts tied to a specific detection rule, analysts can use this
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to prioritize triage and response.
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"""
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from = "now-7205m"
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interval = "5m"
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language = "esql"
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license = "Elastic License v2"
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name = "Newly Observed High Severity Detection Alert"
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risk_score = 73
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rule_id = "1a3d5b36-b995-4ace-9b85-8a0af429ccf6"
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severity = "high"
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tags = ["Use Case: Threat Detection", "Rule Type: Higher-Order Rule", "Resources: Investigation Guide"]
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timestamp_override = "event.ingested"
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type = "esql"
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query = '''
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FROM .alerts-security.*
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| where kibana.alert.rule.name is not null and kibana.alert.risk_score >= 73 and
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not kibana.alert.rule.type in ("threat_match", "machine_learning", "new_terms") and
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not kibana.alert.rule.name like "Deprecated - *" and kibana.alert.rule.name != "My First Rule" and
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// covered by 7306ce7d-5c90-4f42-aa6c-12b0dc2fe3b8
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event.dataset != "endpoint.alerts"
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| STATS Esql.alerts_count = count(*),
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Esql.first_time_seen = MIN(@timestamp),
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Esql.last_time_seen = MAX(@timestamp),
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Esql.process_executable = VALUES(process.executable),
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Esql.cmd_line = VALUES(process.command_line),
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Esql.parent_executable = VALUES(process.parent.executable),
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Esql.file_path_values = VALUES(file.path),
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Esql.file_path_values = VALUES(file.path),
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Esql.dll_path_values = VALUES(dll.path),
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Esql.user_id_values = VALUES(user.id),
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Esql.user_name_values = VALUES(user.name),
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Esql.agent_id_values = VALUES(agent.id),
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Esql.host_id_values = VALUES(host.id),
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Esql.event_module_values = VALUES(event.module),
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Esql.source_ip_values = VALUES(source.ip),
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Esql.agents_distinct_count = COUNT_DISTINCT(agent.id) by kibana.alert.rule.name
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// fist time seen in the last 5 days - defined in the rule schedule Additional look-back time
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| eval Esql.recent = DATE_DIFF("minute", Esql.first_time_seen, now())
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// first time seen is within 10m of the rule execution time
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| where Esql.recent <= 10 and Esql.agents_distinct_count == 1 and Esql.alerts_count <= 10 and (Esql.last_time_seen == Esql.first_time_seen)
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| keep kibana.alert.rule.name, Esql.*
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'''
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note = """## Triage and analysis
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### Investigating Newly Observed High Severity Detection Alert
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This rule surfaces newly observed, low-frequency behavior high severity alerts affecting a single agent within the current day.
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Because the alert has not been seen previously for this rule and host, it should be prioritized for validation to determine
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whether it represents a true compromise or rare benign activity.
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### Investigation Steps
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- Identify the affected host, user and review the associated rule name to understand the behavior that triggered the alert.
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- Validate the user context under which the activity occurred and assess whether it aligns with normal behavior for that account.
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- Refer to the specific rule investiguation guide for further actions.
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### False Positive Considerations
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- Newly deployed or updated software may introduce behavior not previously observed on the host.
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- Administrative scripts or automation tools can trigger behavior-based detections when first introduced.
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- Security tooling, IT management agents, or EDR integrations may generate new behavior alerts during updates or configuration changes.
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- Development or testing environments may produce one-off behaviors that resemble malicious techniques.
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### Response and Remediation
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- If the activity is confirmed malicious, isolate the affected host to prevent further execution or lateral movement.
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- Terminate malicious processes and remove any dropped files or persistence mechanisms.
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- Collect forensic artifacts to understand initial access and execution flow.
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- Patch or remediate any vulnerabilities or misconfigurations that enabled the behavior.
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- If benign, document the finding and consider tuning or exception handling to reduce future noise.
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- Continue monitoring the host and environment for recurrence of the behavior or related alerts."""
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references = ["https://www.elastic.co/docs/solutions/security/detect-and-alert/about-detection-rules"]
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