Sigma needs to be installed and part of the environment variables. Sigma2SplunkAlert needs Sigma for converting the Sigma detection rules into Splunk searches. Sigma2SplunkAlert configuration file containing Splunk alerts configuration valuesĪnd generates a nf configuration.Sigma configuration file with field names and index mapping (see Sigma repository for more info).folder containing Sigma detection rules.Sigma2SplunkAlert combines Sigma with the power of Jinja2 templating to generate a Splunk nf configuration. Sigma2SplunkAlert is an open source project on Github: Sigma2SplunkAlert introduces tokens to use the interesting fields of an alert in the email body. Additionally, Sigma2SplunkAlert supports Splunk alert actions such as Send email or Add to Triggered Alerts. Sigma2SplunkAlert converts multiple Sigma detection rules into a Splunk nf configuration. Deploying multiple Sigma detection rules into Splunk was a time-consuming task. Sigma is the new standard for describing detection rules. Many Security Operations Center (SOC) are using scheduled searches for their detection rules. Replay any dataset to Splunk Enterprise by using our replay.py tool or the UI.This blog post is a tutorial about a newly created tool Sigma2SplunkAlert converter. Initial Confidence and Impact is set by the analytic author. The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Possible false positive scenarios include but are not limited to vulnerability scanners, administration systems and missconfigured systems. Known False PositivesĪn single endpoint requesting a large number of kerberos service tickets is not common behavior. The Advanced Security Audit policy setting Audit Kerberos Authentication Service within Account Logon needs to be enabled. To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. List of fields required to use this analytic. It allows the user to filter out any results (false positives) without editing the SPL. Unusual_number_of_kerberos_service_tickets_requested_filter is a empty macro by default. | `unusual_number_of_kerberos_service_tickets_requested_filter` | eval isOutlier=if(unique_services > 2 and unique_services >= upperBound, 1, 0) | eventstats avg(unique_services) as comp_avg, stdev(unique_services) as comp_std by Client_Address | stats dc(Service_Name) AS unique_services values(Service_Name) as requested_services by _time, Client_Address `wineventlog_security` EventCode=4769 Service_Name!="*$" Ticket_Encryption_Type=0x17 Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud To customize this analytic, users can try different combinations of the bucket span time and the calculation of the upperBound field. The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number service ticket requests. Kerberoasting allows an adversary to request kerberos tickets for domain accounts typically used as service accounts and attempt to crack them offline allowing them to obtain privileged access to the domain. The following hunting analytic leverages Kerberos Event 4769, A Kerberos service ticket was requested, to identify a potential kerberoasting attack against Active Directory networks. Unusual Number of Kerberos Service Tickets Requested
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