Sigma Rules in CTEM

Sigma rule creation plays a key role in Continuous Threat Exposure Management (CTEM) by helping organizations detect and respond to threats more efficiently and proactively.

Sigma Rules

Sigma is a generic and open signature format for writing rules that can detect suspicious behavior in log files. Think of Sigma as the “YARA for logs.” These rules are used to identify patterns of malicious or abnormal behavior across different platforms like SIEMs (e.g., Splunk, ELK, etc.).

How Sigma Rule Creation Helps in CTEM

1. 🔄 Continuous Detection Tuning
   • CTEM involves simulating attacks (like breach and attack simulations) to test an organization’s defenses.
   • By writing Sigma rules that match these simulated behaviors, you ensure your detection tools are alerting on real-world threats.
   • This allows continuous improvement in your detection capabilities.
2. 📊 Threat Coverage Validation
   • Sigma rules can help verify whether known TTPs (Tactics, Techniques, and Procedures) from frameworks like MITRE ATT&CK are being detected.
   • You can compare Sigma rules to threat simulations and identify detection gaps.
3. ⚙️ Automating Detection Engineering
   • In CTEM, threats are continuously evolving.
   • Sigma rules make it easier to quickly write and deploy new detections across multiple systems without vendor lock-in.
4. 🚨 Faster Response Times
   • When Sigma rules are properly tuned, alerts are more accurate, reducing false positives.
   • CTEM relies on fast detection and response to exposures—Sigma rules make that possible.
5. 🧠 Threat Intelligence Operationalization
   • CTEM thrives on threat intelligence.
   • You can convert threat intel into actionable Sigma rules that proactively look for signs of compromise related to current threats.

Example Sigma Rule – Detecting PowerShell Download Cradles

title: PowerShell Download Cradle Detected
id: f9b3c3c8-5c3c-4d10-8c34-9d7fcf32a37a
status: experimental
description: Detects PowerShell commands used to download files from the internet.
author: YourName
date: 2025/04/18
logsource:
category: process_creation
product: windows
detection:
selection:
Image|endswith: powershell.exe
CommandLine|contains:
– “Invoke-WebRequest”
– “Invoke-Expression”
– “DownloadString”
– “New-Object Net.WebClient”
condition: selection
fields:
– CommandLine
– ParentImage
– Image
– User
level: high
tags:
– attack.execution
– attack.t1059.001

Why It Matters for CTEM

In Continuous Threat Exposure Management, this rule would:

1. Validate detection coverage for techniques like T1059.001 (PowerShell).
2. Be tested through attack simulation tools (like Atomic Red Team or Breach & Attack Simulators).
3. Help teams identify gaps in response (e.g., “Did we detect this during the simulation?”).
4. Support rapid tuning and improvement of security tools like SIEMs.

How to Use This in Practice:

• Drop this into your Sigma-compatible detection platform or convert it to the SIEM query language (like Splunk SPL or Elastic DSL).
• Pair it with threat simulations targeting PowerShell execution.
• Track whether alerts fire, and whether your SOC responds in time.

Sigma Rule Example – Suspicious Rundll32 Execution

title: Suspicious Rundll32 Execution
id: 8746e99a-1445-45e0-973c-2f1c1fc9a333
status: stable
description: Detects rundll32.exe executing a suspicious DLL or calling a suspicious export, commonly used for living-off-the-land attacks.
author: YourName
date: 2025/04/18
logsource:
category: process_creation
product: windows
detection:
selection:
Image|endswith: \rundll32.exe
CommandLine|contains:
– “javascript:”
– “shell32.dll,ShellExec_RunDLL”
– “url.dll,FileProtocolHandler”
– “LaunchApplication”
condition: selection
fields:
– CommandLine
– ParentImage
– Image
– User
level: medium
tags:
– attack.defense_evasion
– attack.t1218.011

Converting Sigma Rule to ELK (Elasticsearch Query)

Sigma rules are abstracted so you can map them into SIEM-specific queries. In ELK, you’re typically using Lucene or KQL (Kibana Query Language), often via a Winlogbeat index for Windows logs.

Convert This to a KQL Query (for use in Kibana)

process.name : “rundll32.exe” and
process.command_line : (“javascript:” or “shell32.dll,ShellExec_RunDLL” or “url.dll,FileProtocolHandler” or “LaunchApplication”)

Where to Use It

In Kibana, you’d:

• Go to Discover
• Choose your relevant index (like winlogbeat-*)
• Paste the above KQL query to filter logs
• Or create a detection rule in Elastic Security → Detection Rules using the same logic

Make sure the logs you’re collecting (via Winlogbeat or Elastic Agent) are mapping fields like:

• process.name
• process.command_line
• process.parent.name
• user.name

Otherwise, you’ll need to tweak the query to match your field naming convention.

YARA Rules

YARA is a tool and rule-based language used to identify and classify malware by creating patterns that describe specific file characteristics.

YARA = “Pattern-matching tool for malware and suspicious files.” It’s widely used by:

• Malware analysts
  • Scan suspicious files or folders with YARA rules to check for malware signatures.
• Threat hunters
  • Run YARA rules across systems to hunt for indicators of compromise (IOCs).
• Incident responders
  • YARA can scan running processes to find in-memory malware.
• Antivirus and EDR vendors
  • Many tools (like VirusTotal, Cuckoo Sandbox, Elastic, etc.) support YARA.
  • Often integrated into SIEMs, EDRs, and SOC workflows.

It looks inside files (binaries, scripts, documents, etc.) to find specific strings, byte patterns, or conditions that match known malware or threat behavior.

Here is an example of YARA rule –

rule Example_Malware
{
meta:
author = “YourName”
description = “Detects Example Malware variant”
date = “2025-04-18”

strings:
$s1 = “This program cannot be run in DOS mode”
$s2 = “malicious_function”
$hex = { 6A 40 68 00 30 00 00 6A 14 8D 91 }

condition:
any of ($s*) or $hex
}

The strings section defines what to look for: plain text or hex byte patterns.

The condition defines how those patterns must appear for a file to be a match.

If You’re a SOC analyst:

• If matches are found, initiate your incident response.
• A malware sample is spreading.
• You create a YARA rule to match its unique byte patterns.
• Run the YARA rule across endpoints or incoming files.

YARA vs Sigma: Quick Comparison