Writing and Testing Detection Rules with Sigma and YARA

Effective threat detection requires robust tools and methodologies that can identify malicious activity across different environments.

Sigma and YARA rules have emerged as powerful open-source frameworks for security analysts, allowing standardized detection methods that can be shared across platforms and teams.

While Sigma rules focus on identifying suspicious patterns in log data, YARA rules excel at classifying and detecting malware in files. Together, these complementary approaches form a comprehensive detection strategy for modern security operations centers.

Understanding Sigma and YARA Rules

Sigma rules serve as a generic signature format for log files, functioning similarly to how Snort works for network traffic.

Introduced in 2017, Sigma provides a standardized way to describe relevant log events using human-readable YAML syntax.

This platform-agnostic approach allows security teams to write a detection once and deploy it across multiple SIEM platforms, overcoming the challenge of proprietary query languages.

A typical Sigma rule contains several key components:

• Title and description explaining the detection purpose
• Logsource defining where to look
• Detection section specifying patterns to match
• Condition section defining triggering logic
• Metadata including author, status, and reference information

YARA rules, on the other hand, were designed for malware research and detection. Often described as “the pattern matching Swiss knife for malware researchers,” YARA rules identify textual or binary patterns within files to classify and detect malicious software. Created by Victor Alvarez in 2013, YARA has become an essential tool for threat hunting and incident response.

YARA rules follow a C-like syntax and typically include:

• Rule identifier
• Strings section defining patterns to search for
• Condition section determining when to trigger
• Metadata and tags for classification

Writing Effective Detection Rules

Crafting Sigma Rules

Writing effective Sigma rules requires understanding both the YAML schema and the art of detection engineering. A basic Sigma rule starts with defining the log source (product, category, service) followed by detection components and conditions.

Here’s an example of a Sigma rule detecting a potentially suspicious account lockout:

texttitle: User Account Locked Out
id: 14701da0-4b0f-4ee6-9c95-2ffb4e73bb9a
status: test
description: Detects when a user account is locked out due to excessive failed login attempts.
author: Security Analyst
date: 2025-04-17
tags:
  - attack.impact
logsource:
  product: okta
  service: okta
detection:
  selection:
    displaymessage: Max sign in attempts exceeded
  condition: selection
falsepositives:
  - Legitimate users forgetting passwords
level: medium

This rule looks for specific message patterns in Okta logs that indicate an account lockout event, which could signify a brute force attack attempt. The simplicity of the YAML format makes Sigma rules accessible even to those with limited programming experience.

Developing YARA Rules

YARA rules require a deeper understanding of malware characteristics and file structures. Effective YARA rules balance specificity (to reduce false positives) with flexibility (to catch variants).

Here’s an example of a basic YARA rule to detect potentially suspicious patterns:

textrule Detect_Suspicious_PDF {
    meta:
        description = "Detects potentially malicious PDF documents"
        author = "Security Researcher"
        date = "2025-04-17"
    
    strings:
        $header = "%PDF"
        $js1 = "JavaScript" nocase
        $js2 = "JS" nocase
        $suspicious1 = "eval" nocase
        $suspicious2 = "exploit" nocase
        
    condition:
        $header at 0 and
        ($js1 or $js2) and
        any of ($suspicious*)
}

This rule identifies PDF files containing JavaScript with potentially malicious functions like “eval” that are commonly used in exploits. The rule demonstrates YARA’s ability to combine static identifiers with more complex pattern matching.

Testing and Optimizing Detection Rules

Testing detection rules is critical before deployment in production environments. For Sigma rules, the process involves converting them to your SIEM’s query language using tools like sigmac and running them against historical data to validate effectiveness.

This helps identify false positives and determine appropriate alert thresholds.

For YARA rules, testing typically involves scanning known malicious and clean files to measure detection accuracy. Tools like the YARA command-line scanner or integrated platforms that support YARA can automate this process.

Common optimization strategies include:

1. Refining conditions: Adjust rule logic to minimize false positives while maintaining detection capabilities.
2. Performance tuning: Order conditions to ensure faster execution by placing the most restrictive conditions first.
3. Tiered detection: Implement different severity levels based on confidence, with more specific rules triggering higher alerts.
4. Rule maintenance: Regularly review and update rules as threat techniques evolve and new variants emerge.

Both Sigma and YARA benefit from community collaboration. Open repositories provide thousands of pre-written rules that security teams can leverage and contribute to, creating a collaborative defense ecosystem against emerging threats.

By mastering both Sigma for log-based detection and YARA for file-based analysis, security teams can implement a multi-layered defense strategy that addresses a wide range of potential attack vectors and enhances their overall security posture.

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