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Sigma Rules ​

Sigma rules are YAML files that contain all the information required to detect odd, bad or malicious behaviour when inspecting log files – usually within the context of a SIEM.

To make the most out of the Sigma rules, it is important to understand how Sigma rules are used in detection, what all the different fields mean, and how to start writing and sharing your own Sigma detection rules.

Are you new to Sigma?

If you're unfamiliar with what the Sigma Detection format is, or why it might be useful, visit the About Sigma page to learn more about the project & available toolsets.

Starting Example ​

Below shows a full example of an in-use Sigma rule.
This example detects an "Okta User Account Locked Out" event. [1]

yaml
# ./rules/cloud/okta/okta_user_account_locked_out.yml
title: Okta User Account Locked Out
id: 14701da0-4b0f-4ee6-9c95-2ffb4e73bb9a
status: test
description: Detects when a user account is locked out.
references:
    - https://developer.okta.com/docs/reference/api/system-log/
    - https://developer.okta.com/docs/reference/api/event-types/
author: Austin Songer @austinsonger
date: 2021-09-12
modified: 2022-10-09
tags:
    - attack.impact
logsource:
    product: okta
    service: okta
detection:
    selection:
        displaymessage: Max sign in attempts exceeded
    condition: selection
falsepositives:
    - Unknown
level: medium

Components ​

To help understand what the above code snippet accomplishes, this Sigma rule will be separated into three main components:

  • Detection
    What malicious behaviour the rule searching for.
  • Logsource
    What types of logs this detection should search over.
  • Metadata
    Other information about the detection.

Detection ​

Required

The detection section is the most important component of any Sigma rule. It specifies exactly what the rule is looking for across relevant logs.

yaml
    product: okta
    service: okta
detection: 
    selection:
        displaymessage: Max sign in attempts exceeded
    condition: selection
falsepositives:
    - Unknown

This specific detection above is searching for anywhere an Okta User's account has been locked out, due to the maximum number of sign-in attempts being reached.

Principles ​

The Sigma detection format can be quite daunting and confusing for newcomers, as some Sigma rules can be complex in how they construct detections. It's important to understand the following guiding principles:

Selections ​

Each Sigma detection is categorised & split up into groups called "selections". Each "selection" contains the definition for the detection itself.

yaml
detection:
    selection: 
        displaymessage: Max sign in attempts exceeded
    condition: selection

Sigma use "selection" groups to organise detections for readability & filtering, which becomes important when exploring Conditions.

And / Or ​

The other main consideration, is that Sigma completes "AND" and "OR" operations by using YAML Lists and Dictionaries.

  • The list syntax to represent an "OR" operation, and
yaml
detection:
  selection:
    field_name:
      - this # or
      - that
  condition: selection
  • The dictionary/object syntax to represent an "AND" operation.
yaml
detection:
  selection:
    field_name: this # and
    other_field_name: that
  condition: selection

Detection Methods ​

With both of those topics in mind, explore below on how Sigma composes detections with 3 main patterns.

by Keyword ​

One of the most basic and easy-to-understand ways to detect malicious behaviour using Sigma is by using keywords. Keyword-based searches look over a given logsource for any text matching any of the provided keywords. This corresponds to searching for a string into the raw log.

Each item in the "keywords" list is effectively separated by a logical "OR" operator.

The below example Sigma rule uses a keyword-based search to detect when users might be covering their tracks – by purging that user's .bash_history.

yaml
logsource:
    product: linux
detection:// [!code focus:8]
    keywords:
        - 'rm *bash_history'
        - 'echo "" > *bash_history'
        - 'truncate -s0 *bash_history'
        - 'history -c'
        - 'history -w'
    condition: keywords
falsepositives:
    - Unknown

This example tells Sigma to generate a SIEM query that searches for any of the following keywords:

  • rm *bash_history or
  • echo "" > *bash_history or
  • truncate -s0 *bash_history or
  • history -c or
  • history -w

Naming of keywords

The naming of the field "keywords" under the detection and selection fields in this example is arbitrary. However, you should use it as a standard when creating your own Sigma rules.

When we convert the rule into Splunk Query Language (for example) using the sigma-cli tool, the resulting query after conversion will be the following.

splunk
"rm *bash_history" OR "echo \"\" > *bash_history" OR "truncate -s0 *bash_history" OR "history -c" OR "history -w"

A note on efficiency

Keep in mind that while keyword-based searches are easy to write, most SIEMs will usually perform vastly better when using field-based searches.

by Field ​

Next, Sigma rules can also be used to complete field-value searches.

To do this, represent the fields as a YAML "object" with their respective names and values.

yaml
detection:
  selection:
    Username: "Administrator"
  condition: selection

You can also see where multiple fields appear together, by adding more field-value pairs to the YAML object.

For instance, to detect when a USB device is inserted into a machine, you may need to consider the following events triggered within the Windows Event Logging system:

  • EventID: 6416 A new external device was recognised by the system and
  • where the class of drive was a "DiskDrive"

To search for where both of these occur, place both together within the selection object, as shown below.

yaml
title: External Disk Drive Or USB Storage Device
description: >
    Detects external diskdrives or plugged in USB devices,
    EventID 6416 on windows 10 or later
logsource:
    product: windows
    service: security
detection: 
    selection:
        EventID: 6416  # and where
        ClassName: 'DiskDrive'
    condition: selection
falsepositives:
    - Legitimate administrative activity

The resulting query after conversion will be the following
(Splunk Query Language used as an example):

splunk
source="WinEventLog:Security" EventCode=6416 ClassName="DiskDrive"

by Field List ​

The "by Field List" detection method is similar to the "by Field" method. It is useful when you have to search for multiple values of a field.

For example, you might want to search Windows\Security logs and detect when:

You can represent this in Sigma by using a YAML list, where each field value is prepended by a newline, followed by a "-".

yaml
title: Group Modification Logging
description: Triggers when an account is added to or removed from any group assigned administrative privileges.
logsource:
    product: windows
    service: system
detection: 
    selection:
        EventID:
            - 4728  # or where
            - 4729  # or where
            - 4730
    condition: selection
falsepositives:
    - Unknown

The resulting query after conversion will be the following
(Splunk Query Language used as an example):

splunk
source="WinEventLog:System" EventCode IN (4728, 4729, 4730)

Conditions ​

The Sigma detection section can consist of one or more detections, or "selection" groups. Each selection group must be present in the condition field at the bottom of the detection section.

The default name given to this group is selection, but it is often given more descriptive information about what it's attempting to detect.

yaml
detection:
  selection:
    field: value
  condition: selection

Conditions

It's worth noting that conditions can be used to organize various selection groups, as well as combine or negate with other selection groups to perform powerful detection operations within the SIEM. For instance:

yaml
condition: (selection_one or selection_two) and not filter

If you want to learn more about combining different "selection groups", you can check out the Conditions section of the documentation.

Logsources ​

Required

The logsource section in Sigma rules is used to specify what log data should be searched by the rule. By specifying the appropriate log type in the logsource field, Sigma rules can target a specific variety of logs, rather than searching over all types of logs within a SIEM.

It splits up each defined logsource into three distinct fields - category, product, and service.

  • Category
    This describes a category of products.
    (Eg. webserver, firewall or edr)

  • Product
    This describes a specific product.
    (Eg. windows, linux, cisco)

  • Service
    This describes a service running within a given product
    (Eg. kerberos, defender etc).

yaml
author: Austin Songer @austinsonger
date: 2021-09-12
logsource: 
    product: okta
    service: okta
detection:
    selection:

Sigma rules usually use a standard combination of these fields to target a specific log source.

Logsource definition

The definition field within logsource can also provide more information about how to onboard the log data source correctly.

yaml
tags:
  - attack.privilege_escalation
  - attack.t1548
logsource: 
  product: windows
  category: ps_script
  definition: Script Block Logging must be enabled
detection:
  selection:
    ScriptBlockText|contains: 'Invoke-Nightmare'
LogsourcesLearn how to tune your Sigma rules for specific sets of logs.

Metadata ​

Optional

Everything else that you can see around the logsource and detection sections is what Sigma calls "Metadata", and can include fields such as tags, level, references, description and more.

Sigma Specification

There is a defined Sigma specification that outlines what Sigma considers to be "standard" fields, but it's important to note that Sigma rules can contain any amount of Metadata fields you like.

Available Sigma metadata Fields ​

Below is a list of standard Sigma metadata fields.

Title ​

Required

The title field is used to give a very short summary of what the rule is trying to achieve.

yaml
title: Okta User Account Locked Out

Hint on writing good Alert titles

Try to keep your alert titles as short as possible, and avoid prefixes like "Detects when ...".

ID ​

The id field should be generated whenever you create a Sigma rule, and globally identifies the Sigma rules against all others. For this reason, Sigma recommends using randomly generated UUIDs (version 4).

You can generate your own UUIDv4 by following the link here.

yaml
id: 12345678-bef0-4204-a928-ef5e620d6fcc

Rule ID changes

Rule identifiers should only change for the following reasons:

  • Major changes in the rule. (E.g. a different rule logic.)
  • Derivation of a new rule from an existing or refinement of a rule in a way that both are kept active.
  • Merge of rules.

Status ​

The status field advertises the current state of the Sigma rules as in-development, in-testing or ready for use.

Values: stable | test | experimental | deprecated | unsupported

yaml
status: stable

Description ​

The description field gives some explanation of the reason why the rule is in existence, how to best use it, and when it will trigger.

yaml
description: This rule detects Windows RDP administrative logon

Writing Good Descriptions

Descriptions are often used by other products and services to help susinctly explain what the rule is trying to achieve. Try to avoid using:

  • "Detects when ..." or "Detects if ..."
  • "This rule will ..."

License ​

Optional

The license field optionally specifies a SPDX IDs reference on how the Sigma rule can be used by others within the community.

yaml
license: MIT
# OR
license: GPL-2.0-or-later

Publishing Sigma Rules

When contributing rules to the community, some repositories will usually have a LICENSE file that outlines the license of the entire repository, and all the detection rules pertained inside of it.

Remember to discuss the topic of any possible conflicting licenses with the repository owner before contributing any rules.

Author ​

The author field specifies the singular author of the Sigma rule. It can also include a Twitter handle, email, or other way to contact the author.

yaml
author: Sirens [sirens@sigma.hq]

Date / Modified ​

The date / modified field states the creation / last modified date of the rule.

yaml
date: 2023-01-01
modified: 2023-01-02

Date Format

The date and modified fields should be standardised to use the ISO 8601 date with separator format : YYYY-MM-DD.

When to change the modified date?

You should only change the modified date when you:

  • change the title,
  • change the detection section,
  • change the level or
  • change the logsource

References ​

The references field provides a URL, list of URLs or other text references how, or why the Sigma rule was created by the author.

yaml
references: https://learn.microsoft.com/en-us/windows/security/threat-protection/auditing/event-6416

# OR

references:
    - Plain Text (Eg. CVE-2025-22222)
    - https://www.trendmicro.com/vinfo/hk-en/security/news/cybercrime-and-digital-threats/malicious-spam-campaign-uses-iso-image-files-to-deliver-lokibot-and-nanocore
    - https://www.proofpoint.com/us/blog/threat-insight/threat-actor-profile-ta2719-uses-colorful-lures-deliver-rats-local-languages
    - https://twitter.com/MsftSecIntel/status/1257324139515269121

Tags ​

The tags field applies is used to categorize or map Sigma rules into a variety of different security frameworks and availability standards. These include, but are not limited to:

yaml
tags:
  # MITRE ATT&CK framework references
  - attack.discovery
  - attack.t1012

  # MITRE Cyber Analytics Repository (CAR)
  - car.2016-04-005
  - car-2016-04-005

  # Traffic Light Protocol
  - tlp.amber
  - tlp.green

  # CVE
  - cve.2022-27925

Tags Naming Convention

The Sigma team have defined a tag naming convention under the Sigma Specification repository. You can find more information about this here on the sigma-specification repo.

False Positives ​

The falsepositives field outlines a list of possible known false positives that may occur.

yaml
falsepositives:
  - This rule can sometimes create false-positives when a user forefully restarts their workstation

False Positives vs Filters

False-positives are not parsed by the Sigma convertors, and are simply there to help the detection-engineer or analyst to triage the alert as to when the rule might trigger in a non-malicious context.

If you want to filter out certain events, you should use the condition field in combination with a filter condition in the detection section, or use the newly published Sigma Filters feature to help tune your alerts.

Level ​

The level field describes the criticality of a triggered rule.

While low and medium level events have an informative character, events with high and critical level should lead to immediate reviews by security analysts.

yaml
level: low

Values: critical | high | medium | low | informational

The five existing levels are divided into two categories.

  • Rules that have informative character and should be displayed in a list or bar chart
    (informational,low, medium)

  • Rules that should trigger a dedicated alert
    (high, critical)

Apply the following guidelines when setting a level:

  • Rules of level critical should never trigger a false positive and be of high relevance
  • Rules of level high trigger on threats of high relevance that have to be reviewed manually (rare false positives > baselining required)
  • Rules of level high and critical indicate an incident (if not a false positive)
  • Rules of level low and medium indicate suspicious activity and policy violations
  • Rules of level informational have informative character and are often used for compliance or correlation purposes

  1. Rule sourced from SigmaHQ/sigma repository 8 Dec 2022: https://github.com/SigmaHQ/sigma/blob/master/rules/cloud/okta/okta_user_account_locked_out.yml ↩︎

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