Building Detection Rule With Splunk Spl

使用 Splunk SPL 构建检测规则

概述

Splunk 搜索处理语言（SPL，Search Processing Language）是 Splunk Enterprise Security 中用于构建关联搜索的主要查询语言，用于检测可疑事件和模式。精心设计的检测规则可聚合、关联和富化安全事件，为 SOC 分析师生成可操作的重要事件。企业级 SIEM 平均仅覆盖 MITRE ATT&CK 技术的 21%，这使得熟练编写 SPL 规则对于填补检测空白至关重要。

前置条件

• 已部署并配置 Splunk Enterprise Security（ES）
• 具有适当角色的 Splunk 搜索与报告应用访问权限
• 了解通用信息模型（CIM，Common Information Model）数据模型
• 熟悉 MITRE ATT&CK 框架技术
• 了解组织的日志来源和数据流

核心 SPL 检测规则模式

1. 基于阈值的检测

检测在时间窗口内超过定义计数的事件。

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. 基于序列的检测（失败登录后成功）

关联事件序列以指示成功的暴力破解攻击。

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. 与基线比较的异常检测

将当前活动与基线周期进行比较以检测异常峰值。

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. 横向移动检测

使用 Windows 登录事件识别潜在横向移动。

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. 数据外泄检测

监控大规模出站数据传输。

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 可疑执行检测

检测编码或混淆的 PowerShell 命令。

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

在 Splunk ES 中构建关联搜索

逐步流程

1. 定义用例：映射到 MITRE ATT&CK 技术，定义要检测的行为
2. 识别数据源：确定哪些索引和 sourcetype 包含相关事件
3. 编写基础搜索：构建提取相关事件的 SPL
4. 添加聚合：使用 stats、eventstats 或 streamstats 进行汇总
5. 应用阈值：用 where 子句设置区分正常与异常的条件
6. 富化上下文：添加资产信息、身份数据和威胁情报的查找表
7. 配置重要事件：设置严重性、紧急程度和描述字段
8. 调度与测试：针对历史数据运行并验证检测准确性

关联搜索配置模板

| 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"

富化最佳实践

| 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"
)

性能优化

使用 tstats 搭配数据模型

| 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 *

限制时间范围并使用索引字段

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

使用摘要索引建立历史基线

| 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"

测试与验证

针对已知攻击模式测试

| 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"
]

计算检测指标

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 映射

参考资料

• Splunk ES 关联搜索最佳实践
• 编写实用 Splunk 检测规则
• 配置关联搜索 - Splunk 文档
• SOC Prime - Splunk 中的关联事件