selection | count(dst_ip) by src_ip > 10 Condition and Detection selection | count(dst_port) by src_ip > 10 Condition: The part that deals with how the rule will be processed on platforms. In the field, logic is specified for rule matching, and, or, pipe, count, max, avg etc.Description: Detailed information about what the rule does.Log Source consists of category, type, brand, product and service. In the Logsource field, you can define the product (windows, Symantec), category (proxy, firewall), service (SSH, PowerShell), etc. Title and Logsource: You can specify its status, description and references in the field Title.We will examine the parameters of the rule and their values. While explaining the rule structure, we will first use one of the sample rules.Īs can be seen below, the rule structure is quite simple and straightforward. Let’s start to explain the rule structure below. You can edit them or write rules from scratch. Open the project with any compiler and go to the “rules” directory. We will go through the Webstorm compiler, which is the Yaml plugin, you can use VSCode or Atom if you want.ĭownload the Sigma repo from the link below. There is no rule definition with YARA and it also requires a compiler. Sigma is very easy to use because Sigma uses YAML format, we need to work with a compiler that supports YAML. Grep with Perl-compatible regular expression support.Windows Defender Advanced Threat Protection (WDATP).Sigma rules support the following platforms. Sigma rules can be applied to many platforms. However, there is no open format such as YARA rules in which to share work with others. If the condition (in this case, if selection evaluates to true) applies, the rule will trigger.Generally, everyone collects Log data for analysis, collates them, and processes the rules with the required SIEM or Log collection products. The selection will evaluate logs where the field type equals ’EXECVE’, and a0 contains “chattr” and a1 contains “-i”. Within the condition, logical functions like and/or, not, one of, any of and aggregations expressions (count, min, max, avg and sum) on selectors (which can use wildcards) can be used.Īs an example, this rule detects the removal of immutable file attributes on linux systems logged by auditd. The condition will tie everything together, evaluating the selectors using an expression that is usually complex. An optional timeframe will define the period for the selector to be aggregated on. The map or list will contain the column to test (using wildcards for fields) and a value, with optional transformations (contains, all, base64, startswith, endswith, etc.) or type modifiers (regular expression). It consists of one or more selectors, each selector can be a map (all joined with a logical function and) or list (logical function or) and be nested. The detection is the most important part of the rule, defining when the rule will be triggered. false positives, describing scenarios where false positives could be triggered as help for the analyst.detections (one or more selectors, timeframe and condition).the log source, which defines the source of the log data.status (experimental or normal), the status is being used to filter on experimental or normal rules.metadata (id, tags, author, title, references, level).Use Sigma to share the signature with other threat intel communities.Ī Sigma rule is a YAML file, following the specification which be found here, having the following sections:.Share the signature as an appendix of your analysis.
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