![]() ![]() An example of selection criteria using the “endswith” modifier is shown in the example from Intezer below: Intezer Similarly, the selection criteria itself can become detailed and complex and use value modifiers. The Sigma expression looks for Event 4738: “A user account was changed.” in the Windows logs but only alerts when the PasswordLastSet field is not null. In this case, it just follows the selection criteria, but the condition field can allow for a more complex and detailed rule, as defenders can use logical operators such as AND/OR/NOT to combine different conditions.įor example, the rule below would only trigger when ‘selection’ criteria is met but not what the ‘filter’ specifies. ![]() The condition field determines what conditions must be met for the event to trigger. The falsepositives field isn’t processed by the SIEM application but is instead an indicator for the SOC analyst to be aware of situations that can trigger false positives that may require no immediate remediation. The rule, in this example, will trigger only when the field type is EXECVE and the arguments (a0, a1) passed contain “chattr -i”: SANS Id: 4d28fc3b-2ed7-4cd0-97c7-7a90b463c881 status: testĪ real-life scenario rule would look more akin to the one shown below. The rule titled “Remove Immutable File Attribute” triggers seeing a log event generated by The Linux Audit Demon (‘auditd’) every time immutable file attributes are removed from a file. The selection criteria specified under the “detection” section is a set of key-value pairs. Is your goal, for example, to detect instances of a common string (payload) associated with a particular vulnerability exploit, or to monitor occurrences of a particular Log event? Informative fields like status, author, license and description are optional but recommended.īefore writing a Sigma rule, think of what it is you’re trying to accomplish. The critical field logsource specifies the source of the log data that the rules will run against. The status field specifies whether the rule is considered stable for use in production, for testing, experimental, unsupported, or deprecated. Typically, the id field is specified as a randomly generated universally unique identifier (UUID) value. The id field is supposed to contain a globally unique identifier for the rule. The title field briefly describes what the rule is supposed to do in no more than 256 characters. How to write a Sigma ruleĮvery Sigma rule must have a title and an identifier. It offers a long list of fields and defines what each means: Sigmaįrom basic metadata fields such as the name and author of the rule to functional fields such as timeframe, string-identifier, and log source, Sigma rules allow for advanced monitoring of log events and entries. The possibilities Sigma offers are vast and it therefore helps to familiarize yourself with the Sigma specification. Any log entries matching this rule will trigger an alarm. Incident response professionals, for example, can use Sigma rules to specify some detection criteria. Whereas among analysts, YARA rules are more commonly associated with identifying and classifying malware samples (files) using indicators of compromise (IOCs), Sigma rules focus on detecting log events that match the criteria outlined by the rule. These Sigma rules can then be converted by SIEM products into their distinct, SIEM-specific language, while retaining the logic conveyed by the Sigma rule. As such, defenders can use a “common language” to share detection rules with each other independent of their security arsenal. ![]() A prime advantage of using a standardized format like Sigma is that the rules are cross-platform and work across different security information and event management (SIEM) products. Developed by threat intel analysts Florian Roth and Thomas Patzke, Sigma is a generic signature format for use in SIEM systems. ![]() Sigma rules are textual signatures written in YAML that make it possible to detect anomalies in your environment by monitoring log events that can be signs of suspicious activity and cyber threats. What can make it possible, then, for SOC and threat intel analysts to sift through all this flow of information efficiently and separate malicious activity from daily noise in an automated fashion? A typical corporate network consists of hundreds or thousands of devices generating millions of lines of logs pouring in every minute. ![]()
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