The Detection rules window lists all security rules used for detection creation and provides options for filtering the list and viewing details for each rule. Further options allow you to import rules and create new rules by first duplicating a Sigma rule and then modifying it. This section covers navigation of the Rules page and provides descriptions of the actions you can perform.
When you open the Detection rules page, all rules are listed in the table. Use the search bar to search for specific rules by entering a full or partial name and pressing Return/Enter on your keyboard. The list is filtered and displays matching results.
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Alternatively, you can use the Rule type, Rule severity, and Source dropdown lists to drill down in the alerts and filter for preferred results. You can select multiple options from each list and use all three in combination to narrow results.
There are multiple ways to create rules on the Detection rules page. These methods include manually creating a custom rule, importing a rule, and duplicating an existing rule to customize it. The following sections discuss these methods in detail.
The first method of rule creation is to create a custom rule by manually filling in the necessary fields that complete the rule, using either the Visual Editor or the YAML Editor. To do this, select Create detection rule in the uppper-right corner of the screen. The Create detection rule window opens.
When the Create detection rule window opens, the Visual Editor is displayed by default. The required fields in the Visual Editor correspond to the basic fields found in a YAML file formatted as a Sigma rule. The descriptions in these steps mention this correspondence when it might not be immediately obvious.
In the Rule overview section, enter a name for the rule, a description (optional), and the author of the rule. The Rule name corresponds to title in a Sigma rule formatted in a YAML file. The following image provides an example of the populated fields.
In the Details section, enter the log type for the data source, the rule level, and the rule status. The Log type corresponds to the logsource field (specifically, the logsource: product field), while the rule level and rule status correspond to level and status, respectively. Levels in Sigma rules include informational, low, medium, high, and critical. The following image provides an example.
In the Detection section, specify key-value pairs to represent the fields and their values in the log source, which will be the target for detection. These key-value pairs define the detection. You can represent key values as either a single value or as a list containing multiple values.
To define a simple key-value pair, first place the cursor on the Selection_1 label and replace it with a selection name that describes the key-value pair. Next, enter a preferred field from the log source as the Key, and then use the Modifier dropdown list to define how the value is handled. The following modifiers are available:
You can add fields for mapping a second key-value pair by selecting Add map. Follow the previous guidance in this step to map the key-value pair. The following image shows how this definition for two key-value pairs appears in the Create detection rule window.
To add a second selection, use the Add selection bar following the first selection to open another key-value pair mapping. For this selection, values are provided as a list. As described in the first selection, replace the Selection_2 label with a selection name, enter a field name from the log as the key, and select a modifier from the Modifier dropdown list.
Then, to define a key-value pair using a list rather than a single value, select the List radio button. The Upload file button appears and the text box is expanded to accommodate the list.
In the Condition section, specify the conditions for the selections included in the detection definition. These conditions determine how the defined selections are handled by the detection rule. At least one selection is required. In the case of the preceding example, this means that at least one of the two selections selection_schtasks and selection_rare must be added in the Conditions section.
Once the rule is complete and meets your requirements, select Create detection rule in the lower-right corner of the window to save the rule. A rule ID is automatically assigned to the new rule and appears in the list of detection rules.
Another option for creating a new detection rule is duplicating a Sigma rule and then modifying it to create a custom rule. First search for or filter rules in the Rule name list to locate the rule you want to duplicate. The following image shows the list filtered with a keyword.
After performing any modifications to the rule, select Create detection rule in the lower-right corner of the window. A new, customized rule is created. It appears in the list of rules on the main page of the Detection rules window.
Amazon Simple Storage Service (S3) buckets may be inadvertently set with broader (public) access, which could lead to the loss or theft of confidential information. Based on the complexity of access control list (ACL) configurations, it's extremely difficult to determine the true accessibility of an S3 bucket via Cloudwatch logs.
Being able to see who changed access policies that may impact an S3 buckets accessibility, and when the changes were made, allows you to perform further investigation on the bucket to determine if the access is appropriate and warranted.
Watch the video below where we simulate a few Kerberos attacks using open source tools like Rubeus, Kerbrute, and PurpleSharp. We then collect and analyze the resulting telemetry to test our detections using Splunk in a lab environment built with the Attack Range.
Kerberos, initially named after Cerberus, the three-headed dog in Greek mythology, is a network authentication protocol that allows computers and users to prove their identity through a trusted third party. This trusted third-party, called the Kerberos Distribution Center, issues Kerberos tickets to allow clients access to services and network resources based on their privilege level. Kerberos is the default authentication protocol used on Windows Active Directory networks since the introduction of Windows Server 2003.
There are two types of Kerberos tickets: Ticket Granting Ticket (TGT) and Service Tickets (ST). TGTs are first issued to users as an authentication mechanism after submitting their passwords. Once users attempt to consume Kerberos-based services such as a network share or web server, the TGT is submitted to obtain an ST which the user then presents to initiate a session.
As the backbone of Active Directory authentication, Kerberos is commonly abused by adversaries across the different phases of a breach including initial access, privilege escalation, defense evasion, credential access, lateral movement, etc. The analytic story presented in this blog post groups detection use cases in which the Kerberos protocol is abused. Defenders can leverage these analytics to detect and hunt for behavior commonly presented when attackers engage in Kerberos-based attacks.
An important step for defenders trying to enhance their Kerberos attack coverage is to gain a good understanding of the available telemetry generated by Windows when Kerberos is used (or abused). This intelligence can drive our logging requirements as well as content prioritization.
The Windows advanced audit policy configuration provides defenders with two Kerberos logging categories: Audit Kerberos Authentication Service and Audit Kerberos Service Ticket Operations. Both categories are disabled by default and provide six types of events generated only on domain controllers when specific actions occur. The following are the three most relevant events based on the techniques we tested:
This section describes common Kerberos attacks for which we wrote detections in the new analytic story. We are using ATT&CK Tactics to organize them. Note that this is a work in progress and does not cover all the existing Kerberos attack techniques. Feedback is welcome!
Adversaries may abuse Kerberos to validate if a list of users is a domain user or not. This validation can be stealthy as it does not actually generate failed authentication or lockout events. This can be accomplished by submitting a TGT request with no pre-authentication. If the KDC prompts for authentication, the user is valid.
This analytic leverages Event Id 4768. A Kerberos authentication ticket (TGT) was requested to identify one source endpoint trying to obtain an unusual number of Kerberos TGT tickets for non-existing users. This behavior could represent an adversary abusing the Kerberos protocol to perform a user enumeration attack against an Active Directory environment. When Kerberos is sent a TGT request with no preauthentication for an invalid username, it responds with KRB5KDC_ERR_C_PRINCIPAL_UNKNOWN or 0x6.
Kerberos delegation is an impersonation capability that enables an application to access or consume resources hosted on a different server on behalf of users. While convenient, this Kerberos feature introduces new attack vectors that allow adversaries to abuse accounts or computers trusted for the delegation intending to steal Kerberos Ticket Granting Tickets or obtain unauthorized Kerberos Service Tickets.
This analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the Get-ADComputer commandlet used with specific parameters to discover Windows endpoints with Kerberos Unconstrained Delegation. 152ee80cbc
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