building-detection-rule-with-splunk-spl

name	building-detection-rule-with-splunk-spl
description	Build effective detection rules using Splunk Search Processing Language (SPL) correlation searches to identify security threats in SOC environments.
domain	cybersecurity
subdomain	soc-operations
tags	["splunk","spl","detection-engineering","correlation-search","siem","soc","threat-detection","enterprise-security"]
version	1.0
author	mahipal
license	Apache-2.0
d3fend_techniques	["Executable Denylisting","Execution Isolation","File Metadata Consistency Validation","Content Format Conversion","File Content Analysis"]
nist_csf	["DE.CM-01","DE.AE-02","RS.MA-01","DE.AE-06"]
mitre_attack	["T1059.001","T1003.001","T1021.002","T1110.003","T1053.005","T1048"]
source	https://github.com/mukul975/Anthropic-Cybersecurity-Skills
source_commit	04450304b12645cb2b974ab96d28c0664758a88d

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"