building-detection-rule-with-splunk-spl

Files (8)

• LICENSE
• SKILL.md
• template.md · assets/
• api-reference.md · references/
• standards.md · references/
• workflows.md · references/
• agent.py · scripts/
• process.py · scripts/

SKILL.md (first lines)

# 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.

```spl
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.

```spl
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.

```spl
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.

```spl
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.

```spl
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.

```spl
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

From mukul975/Anthropic-Cybersecurity-Skills, Apache-2.0.

More from mukul975/Anthropic-Cybersecurity-Skills