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Introduction:
The cybersecurity landscape is saturated with proprietary tools, each with its own unique query language and logging format, creating a massive challenge for detection engineers. Sigma has emerged as a game-changing open standard that decouples detection logic from specific platforms, enabling the creation of portable, vendor-agnostic detection rules. This paradigm shift promises to democratize and streamline the entire threat detection lifecycle.
Learning Objectives:
- Understand the core syntax and structure of a Sigma rule.
- Learn how to convert a Sigma rule into a target query language like Splunk SPL.
- Master the process of creating custom Sigma rules for novel threats.
- Explore best practices for organizing and managing a Sigma rule repository.
- Utilize the Sigma CLI for local rule conversion and validation.
You Should Know:
1. Deconstructing a Basic Sigma Rule
A Sigma rule is written in YAML, making it both human and machine-readable. Here is a verified example rule for detecting the execution of a suspicious `reg.exe` process, a common technique in persistence attacks.
title: Suspicious Reg.exe Execution id: 9a8f7b6c-5d4e-3f2a-1b0c-9e8d7c6b5a4e status: test description: Detects execution of reg.exe for adding a key to the Run registry for persistence. references: - https://attack.mitre.org/techniques/T1547/001/ author: Korkei Dembroke date: 2024/06/15 modified: 2024/06/15 tags: - attack.persistence - attack.t1547.001 logsource: category: process_creation product: windows detection: selection: Image|endswith: '\reg.exe' CommandLine|contains: 'add' CommandLine|contains: 'HKLM' filter: CommandLine|contains: 'SYSTEM\CurrentControlSet' condition: selection and not filter falsepositives: - Legitimate system administration tasks level: medium
Step-by-step guide:
logsource: This section defines the context. Here, it’s `process_creation` events on Windows.detection: This is the core logic. The `selection` block identifies key indicators: an image ending in `reg.exe` with a command line containing ‘add’ and ‘HKLM’. The `filter` block excludes a common legitimate pattern.condition: This combines the logic blocks. The rule triggers if the `selection` criteria are met, but the `filter` condition is not.
2. Converting Sigma to Splunk SPL with sigmac
You cannot deploy a YAML file directly to your SIEM. You must convert it using the Sigma Converter (sigmac). This command-line tool translates the generic Sigma logic into a platform-specific query.
Verified Command:
sigmac -t splunk -c tools/config/splunk-windows.yml rules/windows/process_creation/suspicious_reg_execution.yml
Step-by-step guide:
- Install Sigma: Clone the Sigma repository from GitHub (
git clone https://github.com/SigmaHQ/sigma.git`).cd sigma`
<h2 style="color: yellow;">2. Navigate to Directory: - Run the Command: The `-t splunk` flag specifies the target (Splunk). The `-c` flag points to the correct configuration file for Windows data in Splunk. The final argument is the path to your Sigma rule file.
- Output: The command will output a valid Splunk Search Processing Language (SPL) query ready for use.
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Building a Custom Rule for LSASS Memory Dumping
A common attacker technique is dumping the LSASS process memory to harvest credentials. Let’s create a custom Sigma rule for this.
Verified Sigma Rule:
title: LSASS Memory Dumping id: 123e4567-e89b-12d3-a456-426614174000 status: production description: Detects attempts to dump the memory of the LSASS process using tools like ProcDump. logsource: category: process_creation product: windows detection: selection: ParentImage|endswith: - '\procdump.exe' - '\sqldumper.exe' - '\rundll32.exe' CommandLine|contains: 'lsass' condition: selection falsepositives: - Authorized penetration testing - Legitimate debugging by system administrators level: high
Step-by-step guide:
- This rule triggers on the creation of a process where the parent image is a known dumping tool (
procdump.exe, etc.) and the command line includes the string ‘lsass’. - The `|endswith` modifier ensures we catch the full path of the executable. The `|contains` modifier looks for the keyword anywhere in the command line.
- After creating the YAML file, save it and use `sigmac` to convert it for your specific SIEM (e.g., Elasticsearch, Azure Sentinel).
4. Hardening Detection with Field Mappings
Sigma’s power comes from its abstraction, but it relies on correct field mappings defined in configuration files. Understanding these is crucial for accurate conversions.
Verified Configuration Snippet (splunk-windows.yml):
logsource: windows: product: windows process_creation: category: process_creation product: windows mappings: Image: "Image" CommandLine: "CommandLine" ParentImage: "ParentImage" User: "User"
Step-by-step guide:
- This configuration file tells `sigmac` how to map the generic Sigma field names (like
Image) to the actual field names in a Splunk index for Windows events. - If your field names are different (e.g., `process_path` instead of
Image), you must modify this mapping file. Incorrect mappings are a primary source of detection failure.
5. Validating and Testing Your Sigma Rules
Before deployment, validate the syntax and test the output of your rules to prevent logical errors and syntax issues in your SIEM.
Verified Commands using Sigma CLI:
Check the YAML syntax of all rules in a directory sigmac --check-errors -r rules/windows/ Convert a rule to multiple backends to test compatibility sigmac -t splunk,es-qs,qlikview rules/windows/process_creation/win_suspicious_reg_execution.yml List all available targets (SIEMs) sigmac -l
Step-by-step guide:
--check-errors: This flag is essential for validating the YAML structure and Sigma logic of your rules before conversion.- Converting to multiple targets (
splunk,es-qs) allows you to see how the same logic is expressed in Splunk SPL and Elasticsearch Query DSL, helping you understand the translation process. - Use `sigmac -l` to discover all the SIEM and query languages Sigma supports.
6. Leveraging Sigma for Cloud Threat Detection (AWS)
Sigma is not limited to endpoint detection. It can be applied to cloud logs, such as AWS CloudTrail, to detect malicious activity in your infrastructure.
Verified Sigma Rule for AWS:
title: AWS Security Group Modification by Unauthorized User id: 567e4567-e89b-12d3-a456-426614174999 status: test description: Detects modifications to AWS Security Groups from a user not in the authorized admin list. references: - https://attack.mitre.org/techniques/T1562/008/ logsource: category: cloud_trail product: aws detection: selection: eventName: AuthorizeSecurityGroupIngress eventName: RevokeSecurityGroupIngress filter: userIdentity.userName: - 'authorized-admin-1' - 'authorized-admin-2' condition: selection and not filter level: high
Step-by-step guide:
- The `logsource` is now `cloud_trail` for
aws. - The `selection` looks for specific high-risk API calls. The `filter` defines an allow-list of authorized users.
- The condition triggers if a risky API call is made by a user not on the allow-list. Converting this with `sigmac -t splunk -c splunk-aws.yml` would generate the appropriate SPL for CloudTrail data.
What Undercode Say:
- The Abstraction is the Advantage. The true value of Sigma is not in the individual rules but in creating a centralized, vendor-neutral knowledge base of detection logic. This breaks the vendor lock-in that has plagued security teams for years.
- Collaboration is Forced and Accelerated. By using a common language, teams can share, peer-review, and improve detections at a scale previously impossible. A rule written by one person for Elasticsearch can be instantly valuable to another team running ArcSight, dramatically increasing collective defense capabilities.
The transition to Sigma represents a fundamental maturation of the security industry, moving from tool-specific silos to a unified theory of detection engineering. While the initial setup and field mapping require effort, the long-term payoff in agility, collaboration, and resilience against a changing toolset is immeasurable. It forces organizations to think more abstractly about the “what” of an attack rather than the “how” of a specific query, leading to more robust and intelligent detection programs.
Prediction:
The adoption of Sigma will become the baseline for mature Security Operations Centers within the next three to five years. As the library of public rules expands, it will create a de facto standard for detection coverage, similar to how MITRE ATT&CK provided a common taxonomy for adversary behavior. We will see the emergence of commercial services and integrated platforms built natively around the Sigma standard, further cementing its role as the foundational layer for agile and resilient cyber defense. Failure to adopt this agnostic approach will leave organizations struggling with inefficient, brittle, and tool-dependent detection pipelines that cannot keep pace with the modern threat landscape.
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IT/Security Reporter URL:
Reported By: Krdmnbrk Splunk – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
