Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
338 detections
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The analytic identifies the execution of the native mimikatz.exe binary on Windows systems, including instances where the binary is renamed. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and original file names. This activity is significant because Mimikatz is a widely used tool for extracting authentication credentials, posing a severe security risk. If confirmed malicious, this activity could allow attackers to obtain sensitive credentials, escalate privileges, and move laterally within the network, leading to potential data breaches and system compromise.
Detects large data transfers over SSH/SCP (port 22) to or from an ESXi host. This activity is highly suspicious and could indicate an attacker delivering a ransomware payload to the hypervisor or exfiltrating sensitive data (like VMDK files).
This rule identifies network traffic to known Command and Control (C2) infrastructure associated with the Katz Stealer malware family. It uses a list of hardcoded IP addresses and domains from threat intelligence to detect suspicious outbound connections.
Detects network traffic to or from IP addresses known to be associated with the active exploitation of the SharePoint vulnerability CVE-2025-53770.
Detects network traffic to command and control (C2) servers associated with the Katz Stealer malware, identified by known IP addresses and domains.
Detects network traffic to known Command and Control (C2) IP addresses associated with the SuperBlack ransomware.
Detects network traffic to known Command and Control (C2) IP addresses used by the Mora_001 group for their SuperBlack ransomware operations.
Detects network connections to domains matching patterns commonly used by the Scattered Spider threat group for phishing campaigns.
The group often registers domains that mimic legitimate corporate services by appending suffixes like "-sso", "-servicedesk", "-okta", "-vpn", or "-helpdesk" to a target's name (e.g., victim-sso.com).
The group often registers domains that mimic legitimate corporate services by appending suffixes like "-sso", "-servicedesk", "-okta", "-vpn", or "-helpdesk" to a target's name (e.g., victim-sso.com).
Detects HTTP requests with URL patterns associated with various malicious Traffic Distribution Systems (TDS) as identified in the Infoblox report on VexTrio and related adtech cabals. These TDS are used to redirect users to malicious content, including scams and malware.
Detects network connections to domains and IP addresses associated with the HelloTDS Traffic Direction System (TDS) infrastructure. This TDS is known for delivering FakeCaptcha, tech scams, and other malware by fingerprinting visitors and redirecting them to malicious landing pages.
Detects DNS queries to domains associated with the VexTrio Traffic Distribution System (TDS) and related malicious adtech platforms as detailed in Infoblox research. These platforms are used to redirect users from compromised websites to scams, malware, and other malicious content.
'Identifies contacts with domains names in CommonSecurityLog that might have been generated by a Domain Generation Algorithm (DGA). DGAs can be used by malware to generate rendezvous points that are difficult to predict in advance.
This detection uses the Alexa Top 1 million domain names to build a model of what normal domains look like. It uses this to identify domains that may have been randomly generated by an algorithm.
The triThreshold is set to 500 - increase this to report on domains that are less likely to have been randomly generated, decrease it for more likely.
The start time and end time look back over 6 hours of data and the dgaLengthThreshold is set to 8 - meaning domains whose length is 8 or more are reported.
NOTE - The top1M csv zip file used in the query is dynamic and may produce different results over various time periods. It's important to cross-check the events against the entities involved in the incident.'
This detection uses the Alexa Top 1 million domain names to build a model of what normal domains look like. It uses this to identify domains that may have been randomly generated by an algorithm.
The triThreshold is set to 500 - increase this to report on domains that are less likely to have been randomly generated, decrease it for more likely.
The start time and end time look back over 6 hours of data and the dgaLengthThreshold is set to 8 - meaning domains whose length is 8 or more are reported.
NOTE - The top1M csv zip file used in the query is dynamic and may produce different results over various time periods. It's important to cross-check the events against the entities involved in the incident.'
'This creates an incident in the event a client generates excessive amounts of DNS queries for non-existent domains.
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM DNS schema'
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM DNS schema'
'Identifies callouts to Discord CDN addresses for risky file extensions. This detection will trigger when a callout for a risky file is made to a discord server that has only been seen once in your environment.
Unique discord servers are identified using the server ID that is included in the request URL (DiscordServerId in query). Discord CDN has been used in multiple campaigns to download additional payloads.
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM WebSession schema (ASIM WebSession Schema)'
Unique discord servers are identified using the server ID that is included in the request URL (DiscordServerId in query). Discord CDN has been used in multiple campaigns to download additional payloads.
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM WebSession schema (ASIM WebSession Schema)'
'Identifies IP addresses performing DNS lookups associated with common ToR proxies.
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM DNS schema'
This analytic rule uses [ASIM](https://aka.ms/AboutASIM) and supports any built-in or custom source that supports the ASIM DNS schema'
The analytic detects outbound SMB (Server Message Block) connections from internal hosts to external servers. It identifies this activity by monitoring network traffic for SMB requests directed towards the Internet, which are unusual for standard operations. This detection is significant for a SOC as it can indicate an attacker's attempt to retrieve credential hashes through compromised servers, a key step in lateral movement and privilege escalation. If confirmed malicious, this activity could lead to unauthorized access to sensitive data and potential full system compromise.
This rule identifies a source that repeatedly fails to authenticate to a web service (HTTP response code 403). This may indicate a [brute force](https://en.wikipedia.org/wiki/Brute-force_attack) or [credential stuffing](https://en.wikipedia.org/wiki/Credential_stuffing) attack.
This rule uses the [Advanced Security Information Model (ASIM)](https://aka.ms/AboutSIM) and supports any web session source that complies with ASIM.
This rule uses the [Advanced Security Information Model (ASIM)](https://aka.ms/AboutSIM) and supports any web session source that complies with ASIM.
'This composite hunting query will highlight any HTTP traffic in CommonSecurityLog web proxies (such as ZScaler) that match known patterns used by red teaming tools potentially stolen from FireEye. Most FireEye red teaming tools are designed to mimic
legitimate API activity, false positives are common. This query includes a basic check to determine how common a hostname is in you environment, and allows you to modify this threshold to remove legitimate traffic from the query results.
This query contains only a subset of potential FireEye red team tool communications, and therefore should not be relied upon alone :) .'
legitimate API activity, false positives are common. This query includes a basic check to determine how common a hostname is in you environment, and allows you to modify this threshold to remove legitimate traffic from the query results.
This query contains only a subset of potential FireEye red team tool communications, and therefore should not be relied upon alone :) .'
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.
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