building-detection-rule-with-splunk-spl（使用 splunk spl 构建检测规则）

Building Detection Rules with Splunk SPL

Overview

Splunk Search Processing Language (SPL) is the primary query language used in Splunk Enterprise Security for building correlation searches that detect suspicious events and patterns. A well-crafted detection rule aggregates, correlates, and enriches security events to generate actionable notable events for SOC analysts. Enterprise SIEMs on average cover only 21% of MITRE ATT&CK techniques, making skilled SPL rule writing essential for closing detection gaps.

When to Use

• When deploying or configuring building detection rule with splunk spl capabilities in your environment
• When establishing security controls aligned to compliance requirements
• When building or improving security architecture for this domain
• When conducting security assessments that require this implementation

Prerequisites

• Splunk Enterprise Security (ES) deployed and configured
• Access to Splunk Search & Reporting app with appropriate roles
• Understanding of Common Information Model (CIM) data models
• Familiarity with MITRE ATT&CK framework techniques
• Knowledge of the organization's log sources and data flows

Core SPL Detection Rule Patterns

1. Threshold-Based Detection

Detects events exceeding a defined count within a time window.

index=wineventlog sourcetype=WinEventLog:Security EventCode=4625
| stats count as failed_logins dc(TargetUserName) as unique_users by src_ip
| where failed_logins > 10 AND unique_users > 3
| eval severity="high"
| eval description="Brute force attack detected from ".src_ip." with ".failed_logins." failed logins across ".unique_users." accounts"

2. Sequence-Based Detection (Failed Login Followed by Success)

Correlates a sequence of events indicating a successful brute force attack.

index=wineventlog sourcetype=WinEventLog:Security (EventCode=4625 OR EventCode=4624)
| eval login_status=case(EventCode=4625, "failure", EventCode=4624, "success")
| stats count(eval(login_status="failure")) as failures count(eval(login_status="success")) as successes latest(_time) as last_event by src_ip, TargetUserName
| where failures > 5 AND successes > 0
| eval description="Account ".TargetUserName." compromised via brute force from ".src_ip
| eval urgency="critical"

3. Anomaly Detection with Baseline Comparison

Compares current activity against a baseline period to detect spikes.

index=proxy sourcetype=squid
| bin _time span=1h
| stats count as current_count by src_ip, _time
| join src_ip type=left [
    search index=proxy sourcetype=squid earliest=-7d@d latest=-1d@d
    | stats avg(count) as avg_count stdev(count) as stdev_count by src_ip
]
| eval threshold=avg_count + (3 * stdev_count)
| where current_count > threshold
| eval deviation=round((current_count - avg_count) / stdev_count, 2)
| eval description="Anomalous web traffic from ".src_ip." - ".deviation." standard deviations above baseline"

4. Lateral Movement Detection

Identifies potential lateral movement using Windows logon events.

index=wineventlog sourcetype=WinEventLog:Security EventCode=4624 Logon_Type=3
| where NOT match(TargetUserName, ".*\$$")
| stats dc(dest) as unique_hosts values(dest) as hosts by src_ip, TargetUserName
| where unique_hosts > 5
| eval severity=case(unique_hosts > 20, "critical", unique_hosts > 10, "high", true(), "medium")
| eval description=TargetUserName." accessed ".unique_hosts." unique hosts from ".src_ip." via network logon"

5. Data Exfiltration Detection

Monitors for large outbound data transfers.

index=firewall sourcetype=pan:traffic action=allowed direction=outbound
| stats sum(bytes_out) as total_bytes_out dc(dest_ip) as unique_destinations by src_ip, user
| eval total_mb=round(total_bytes_out/1048576, 2)
| where total_mb > 500 OR unique_destinations > 50
| lookup asset_lookup ip as src_ip OUTPUT asset_category, asset_owner
| eval severity=case(total_mb > 2000, "critical", total_mb > 1000, "high", true(), "medium")
| eval description=user." transferred ".total_mb."MB to ".unique_destinations." unique destinations"

6. PowerShell Suspicious Execution Detection

Detects encoded or obfuscated PowerShell commands.

index=wineventlog sourcetype=WinEventLog:Security EventCode=4104
| where match(ScriptBlockText, "(?i)(encodedcommand|invoke-expression|iex|downloadstring|frombase64string|net\.webclient|invoke-webrequest|bitstransfer|invoke-mimikatz|invoke-shellcode)")
| eval decoded_length=len(ScriptBlockText)
| stats count values(ScriptBlockText) as commands by Computer, UserName
| where count > 0
| eval severity="high"
| eval mitre_technique="T1059.001"
| eval description="Suspicious PowerShell execution on ".Computer." by ".UserName

Building Correlation Searches in Splunk ES

Step-by-Step Process

1. Define the Use Case: Map to MITRE ATT&CK technique and define what behavior to detect
2. Identify Data Sources: Determine which indexes and sourcetypes contain relevant events
3. Write the Base Search: Build SPL that extracts relevant events
4. Add Aggregation: Use stats, eventstats, or streamstats to summarize
5. Apply Thresholds: Set conditions with where clause that distinguish normal from anomalous
6. Enrich Context: Add lookups for asset information, identity data, and threat intelligence
7. Configure Notable Event: Set severity, urgency, and description fields
8. Schedule and Test: Run against historical data and validate detection accuracy

Correlation Search Configuration Template

| tstats summariesonly=true count from datamodel=Authentication
    where Authentication.action=failure
    by Authentication.src, Authentication.user, _time span=5m
| rename "Authentication.*" as *
| stats count as total_failures dc(user) as unique_users values(user) as targeted_users by src
| where total_failures > 20 AND unique_users > 5
| lookup dnslookup clientip as src OUTPUT clienthost as src_dns
| lookup asset_lookup ip as src OUTPUT priority as asset_priority, category as asset_category
| eval urgency=case(asset_priority=="critical", "critical", asset_priority=="high", "high", true(), "medium")
| eval rule_name="Brute Force Against Multiple Accounts"
| eval rule_description="Multiple authentication failures from ".src." targeting ".unique_users." unique accounts"
| eval mitre_attack="T1110.001 - Password Guessing"

Enrichment Best Practices

| lookup identity_lookup identity as user OUTPUT department, manager, risk_score as user_risk
| lookup asset_lookup ip as src_ip OUTPUT asset_name, asset_category, asset_priority, asset_owner
| lookup threatintel_lookup ip as src_ip OUTPUT threat_type, threat_confidence, threat_source
| eval context=case(
    isnotnull(threat_type), "Known threat: ".threat_type,
    user_risk > 80, "High-risk user: risk score ".user_risk,
    asset_priority=="critical", "Critical asset: ".asset_name,
    true(), "Standard context"
)

Performance Optimization

Use Data Models with tstats

| tstats summariesonly=true count from datamodel=Network_Traffic
    where All_Traffic.action=allowed
    by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.dest_port, _time span=1h
| rename "All_Traffic.*" as *

Limit Time Ranges and Use Indexed Fields

index=wineventlog source="WinEventLog:Security" EventCode=4688
    earliest=-15m latest=now()
| where NOT match(New_Process_Name, "(?i)(svchost|csrss|lsass|services)")

Use Summary Indexing for Historical Baselines

| tstats count from datamodel=Authentication where Authentication.action=failure by Authentication.src, _time span=1h
| collect index=summary source="auth_failure_baseline" marker="report_name=auth_failure_hourly"

Testing and Validation

Test Against Known Attack Patterns

| makeresults count=1
| eval src_ip="10.0.0.50", failed_logins=25, unique_users=8, severity="high"
| eval description="Test brute force detection"
| append [
    search index=wineventlog sourcetype=WinEventLog:Security EventCode=4625
    earliest=-24h latest=now()
    | stats count as failed_logins dc(TargetUserName) as unique_users by src_ip
    | where failed_logins > 10 AND unique_users > 3
    | eval severity="high"
]

Calculate Detection Metrics

index=notable
| search rule_name="Brute Force*"
| stats count as total_alerts count(eval(status_label="Closed - True Positive")) as true_positives count(eval(status_label="Closed - False Positive")) as false_positives by rule_name
| eval precision=round(true_positives / (true_positives + false_positives) * 100, 2)
| eval fpr=round(false_positives / total_alerts * 100, 2)

MITRE ATT&CK Mapping

References

• Splunk ES Correlation Searches Best Practices
• Writing Practical Splunk Detection Rules
• Configure Correlation Searches - Splunk Documentation
• SOC Prime - Correlation Events in Splunk