Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This query visualises the total amount of admin false negative submissions by the state of the submission.
This query visualises the original detection technology of emails submitted as phish false positive by admins
This query visualises the amount of emails subject to an admin policy with action of block, summarizing the data daily
'This alerts when the account setting is changed to allow either external domain access or anonymous access to meetings.'
'Detects the redirect URL of an app being changed.
Applications associated with URLs not controlled by the organization can pose a security risk.
Ref: https://docs.microsoft.com/azure/active-directory/fundamentals/security-operations-applications#application-configuration-changes'
Applications associated with URLs not controlled by the organization can pose a security risk.
Ref: https://docs.microsoft.com/azure/active-directory/fundamentals/security-operations-applications#application-configuration-changes'
This query visualises the daily amount of admin false negative submission by submission type.
This query helps report on email access by administrators
'This hunting query identifies new domains added to the domain login whitelist in Zoom.'
This query helps in reviewing malicious emails allowed due to admin overrides
This query visualises the total amount of admin false negative or false positive submissions by the verdict of the submission grading.
This query visualises the total amount of admin false positive submission by submission type.
This query visualises the top admins performing false positive submissions
This query surfaces devices that were discovered by Microsoft Defender for Endpoint and can be onboarded
'This hunting query identifies Accounts in GitHub that have granted access to another account which then grants access to yet another account that may be a sign of compromise.'
'Identifies when a new user is granted access and any subsequent audit related activity. This can help you identify rogue or malicious user behavior.'
The analytic detects cloud API calls executed by user roles that have not previously run these commands. It leverages the Change data model in Splunk to identify commands executed by users with the user_type of AssumedRole and a status of success. This activity is significant because new commands from different user roles can indicate potential malicious activity or unauthorized actions. If confirmed malicious, this behavior could lead to unauthorized access, data breaches, or other damaging outcomes by exploiting new or unmonitored commands within the cloud environment.
The following analytic detects the creation of the doas.conf file on a Linux host. This file is used by the doas utility to allow standard users to perform tasks as root, similar to sudo. The detection leverages Linux Auditd data, focusing on the creation of the doas.conf file. This activity is significant because it can indicate an attempt to gain elevated privileges, potentially by an adversary. If confirmed malicious, this could allow an attacker to execute commands with root commands with root privileges, leading to full system compromise.
The following analytic detects a spike in the number of API calls made to cloud security groups by a user. It leverages data from the Change data model, focusing on successful firewall-related changes. This activity is significant because an abnormal increase in security group API calls can indicate potential malicious activity, such as unauthorized access or configuration changes. If confirmed malicious, this could allow an attacker to manipulate security group settings, potentially exposing sensitive resources or disrupting network security controls.
The following analytic identifies the registration of a new Multi-Factor Authentication (MFA) method for an AWS account, as logged through Amazon Security Lake (ASL). It detects this activity by monitoring the `CreateVirtualMFADevice` API operation within ASL logs. This behavior is significant because adversaries who gain unauthorized access to an AWS account may register a new MFA method to maintain persistence. If confirmed malicious, this activity could allow attackers to secure their access, making it harder to detect and remove their presence from the compromised environment.
The analytic identifies cloud instances being modified by users who have not previously modified them. This activity is significant because it can indicate unauthorized or suspicious changes by potentially compromised or malicious users. If confirmed malicious, this could lead to unauthorized access, configuration changes, or potential disruption of cloud services, posing a significant risk to the organization's cloud infrastructure.
Looks for a new device registration in Entra ID preceded by medium or high-risk sign-in session for the same user within maximum 6h timeframe.
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