Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
123 detections
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The following analytic identifies the use of PowerShell's Start-Service or Stop-Service cmdlets on an endpoint. It leverages PowerShell Script Block Logging to detect these commands. This activity is significant because attackers can manipulate services to disable or stop critical functions, causing system instability or disrupting business operations. If confirmed malicious, this behavior could allow attackers to disable security services, evade detection, or disrupt essential services, leading to potential system downtime and compromised security.
Roasting
KQL
Detect Active Directory LDAP queries that search for Kerberoasting (SPNs) or accounts with Kerberos preauthentication not required from Azure ATP, and try to get the process initiated the LDAP query from MDATP.
Replace 389 on line 5 with LDAP port in your environment
Replace true on line 6 to false if you want to include Nt Authority process
This LDAP query cover Rubeus, Kerberoast, BloodHound tools
Replace 389 on line 5 with LDAP port in your environment
Replace true on line 6 to false if you want to include Nt Authority process
This LDAP query cover Rubeus, Kerberoast, BloodHound tools
// Detect multiple sensitive Active Directory LDAP queries made in bin time
// Sensitive queries defined as Roasting or sensitive objects queries
// Replace 10 on line 6 with your desired thershold
// Replace 1m on line 7 with your desired bin time
// This LDAP query cover Rubeus, Kerberoast, BloodHound tools
// Sensitive queries defined as Roasting or sensitive objects queries
// Replace 10 on line 6 with your desired thershold
// Replace 1m on line 7 with your desired bin time
// This LDAP query cover Rubeus, Kerberoast, BloodHound tools
Detect Active Directory LDAP queries that search for sensitive objects in the organization
This LDAP query cover BloodHound tool
This LDAP query cover BloodHound tool
'This query searches for successful user logins from different countries within 3 hours.
To use this analytics rule, make sure you have deployed the [ASIM normalization parsers](https://aka.ms/ASimAuthentication)'
To use this analytics rule, make sure you have deployed the [ASIM normalization parsers](https://aka.ms/ASimAuthentication)'
Detect Active Directory LDAP queries that search for users with comment or description that contains the string "pass" that might suggest for the user password
This LDAP query cover MetaSploit - enum_ad_user_comments tool
This LDAP query cover MetaSploit - enum_ad_user_comments tool
Detect multiple Active Directory LDAP queries made in bin time
Replace 10 on line 1 with your desired thershold
Replace 1m on line 2 with your desired bin time
Replace 10 on line 1 with your desired thershold
Replace 1m on line 2 with your desired bin time
Detect Active Directory LDAP queries that try to find operating systems that are vulnerable to specific vulnerabilities
This LDAP query cover MetaSploit - enum_ad_computers tool
This LDAP query cover MetaSploit - enum_ad_computers tool
This analytic detects suspicious modifications to Unix shell configuration files, such as `.bashrc` or `.profile`, which may indicate an attempt to alter system behavior or gain unauthorized access. Unauthorized changes to these files can be used to execute malicious commands, escalate privileges, or hide malicious activities. By monitoring for unusual or unauthorized modifications to shell configuration files, this analytic helps identify potential security threats, allowing security teams to respond quickly and mitigate risks.
The following analytic detects attempts to stop a service on Linux systems. It leverages data from Linux Auditd. This activity is significant as adversaries often stop or terminate security or critical services to disable defenses or disrupt operations, as seen in malware like Industroyer2. If confirmed malicious, this could lead to the disabling of security mechanisms, allowing attackers to persist, escalate privileges, or deploy destructive payloads, severely impacting system integrity and availability.
The following analytic detects the suspicious auditd service stop. This behavior is critical for a SOC to monitor because it may indicate attempts to gain unauthorized access or maintain control over a system. Such actions could be signs of malicious activity. If confirmed, this could lead to serious consequences, including a compromised system, unauthorized access to sensitive data, or even a wider breach affecting the entire network. Detecting and responding to these signs early is essential to prevent potential security incidents.
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 analytic detects the creation of cloud compute instances using previously unseen image IDs. It leverages cloud infrastructure logs to identify new image IDs that have not been observed before. This activity is significant because it may indicate unauthorized or suspicious activity, such as the deployment of malicious payloads or unauthorized access to sensitive information. If confirmed malicious, this could lead to data breaches, unauthorized access, or further compromise of the cloud environment. Immediate investigation is required to determine the legitimacy of the instance creation and to mitigate potential threats.
This analytic aggregates and summarizes alerts from Microsoft Defender for Endpoint. It extracts details such as source, file name, severity, process command line, IP address, registry key, signature, description, and unique ID. The detection is not intended to identify new activity from raw data but rather to leverage existing Microsoft Defender for Endpoint alerts for correlation and risk-based alerting. It filters out alerts with a 'clean' verdict and dynamically maps MITRE techniques and assigns risk scores based on the alert's severity.
The following analytic identifies when a new device is enrolled on an Okta account. It uses OktaIm2 logs ingested via the Splunk Add-on for Okta Identity Cloud to detect the creation of new device enrollments. This activity is significant as it may indicate a legitimate user settinging up a new device or an adversary adding a device to maintain unauthorized access. If confirmed malicious, this could lead to potential account takeover, unauthorized access, and persistent control over the compromised Okta account. Monitoring this behavior is crucial for detecting and mitigating unauthorized access attempts.
This correlation rule identifies when a user exceeds a predefined risk threshold based on multiple suspicious Okta activities within a 24-hour period. It aggregates risk events from "Suspicious Okta Activity," "Okta Account Takeover," and "Okta MFA Exhaustion" analytic stories. The detection is significant as it highlights potentially compromised user accounts exhibiting multiple tactics, techniques, and procedures (TTPs). If confirmed malicious, this activity could indicate a serious security breach, allowing attackers to gain unauthorized access, escalate privileges, or persist within the environment.
The analytic identifies a statistically significant increase in access to Active Directory objects, which may indicate attacker enumeration. It leverages Windows Security Event Code 4662 to monitor and analyze access patterns, comparing them against historical averages to detect anomalies. This activity is significant for a SOC because abnormal access to AD objects can be an early indicator of reconnaissance efforts by an attacker. If confirmed malicious, this behavior could lead to unauthorized access, privilege escalation, or further compromise of the Active Directory environment.
The following analytic detects attempts to disable multi-factor authentication (MFA) for an Azure AD user. It leverages Azure Active Directory AuditLogs to identify the "Disable Strong Authentication" operation. This activity is significant because disabling MFA can allow adversaries to maintain persistence using compromised accounts without raising suspicion. If confirmed malicious, this action could enable attackers to bypass an essential security control, potentially leading to unauthorized access and prolonged undetected presence in the environment.
The analytic detects the registration of a new Multi-Factor Authentication (MFA) method for an Azure AD account. It leverages Azure AD AuditLogs to identify when a user registers new security information. This activity is significant because adversaries who gain unauthorized access to an account may add their own MFA method to maintain persistence. If confirmed malicious, this could allow attackers to bypass existing security controls, maintain long-term access, and potentially escalate their privileges within the environment.
The following analytic identifies suspicious command-line parameters associated with the use of Impacket's smbexec.py for lateral movement. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns indicative of Impacket tool usage. This activity is significant as both Red Teams and adversaries use Impacket for remote code execution and lateral movement. If confirmed malicious, this activity could allow attackers to execute commands on remote endpoints, potentially leading to unauthorized access, data exfiltration, or further compromise of the network.
The following analytic identifies excessive AccessDenied events within an hour timeframe for IAM users in AWS. It leverages AWS CloudTrail logs to detect multiple failed access attempts from the same source IP and user identity. This activity is significant as it may indicate that an access key has been compromised and is being misused for unauthorized discovery actions. If confirmed malicious, this could allow attackers to gather information about the AWS environment, potentially leading to further exploitation or privilege escalation.
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