Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects repeated network policy access denials. NPS governs RADIUS-based access to
VPN and wireless, so a burst of denials for one identity is credential testing
against the remote access perimeter — often the first move against an organisation
from outside.
Onboarding: MANAGE-ENGINE-ADAUDIT-PLUS is ingested via Syslog (KV) (Bloo extractor 830).
Integration guide: https://bloo.io/docs/hypercloud/syslog-connector
BQL (Bloo Query Language):
stream=win-audit where sourcename='MANAGE-ENGINE-ADAUDIT-PLUS'
and action='NPS_USER_DENIED'
| duration 15m
| groupby user, networkpolicyname, reasoncode
| select user, networkpolicyname, reasoncode, count(*) as cnt
| having cnt > 10
VPN and wireless, so a burst of denials for one identity is credential testing
against the remote access perimeter — often the first move against an organisation
from outside.
Onboarding: MANAGE-ENGINE-ADAUDIT-PLUS is ingested via Syslog (KV) (Bloo extractor 830).
Integration guide: https://bloo.io/docs/hypercloud/syslog-connector
BQL (Bloo Query Language):
stream=win-audit where sourcename='MANAGE-ENGINE-ADAUDIT-PLUS'
and action='NPS_USER_DENIED'
| duration 15m
| groupby user, networkpolicyname, reasoncode
| select user, networkpolicyname, reasoncode, count(*) as cnt
| having cnt > 10
Detects RADIUS authentication activity observed in network traffic, including the
MAC address of the requesting device. Seeing RADIUS on the wire catches
authentication against network infrastructure that often logs nowhere else —
switches, wireless controllers and VPN concentrators.
Onboarding: NTA is ingested via Network Traffic Analysis (Bloo extractor 717).
Integration guide: https://bloo.io/docs/integrations
BQL (Bloo Query Language):
stream=nta-authentication-radius where sourcename='NTA'
| duration 1h
| groupby srcip, dstip, user, macaddress
MAC address of the requesting device. Seeing RADIUS on the wire catches
authentication against network infrastructure that often logs nowhere else —
switches, wireless controllers and VPN concentrators.
Onboarding: NTA is ingested via Network Traffic Analysis (Bloo extractor 717).
Integration guide: https://bloo.io/docs/integrations
BQL (Bloo Query Language):
stream=nta-authentication-radius where sourcename='NTA'
| duration 1h
| groupby srcip, dstip, user, macaddress
Detects the use of database or archiving command-line utilities to export data related to RADIUS services. The rule filters out known backup service accounts and authorized backup processes, flagging activity that occurs outside of standard business hours or is performed by accounts not explicitly designated for administrative or database maintenance tasks.
Detects the use of database or archiving command-line utilities to export data related to RADIUS services. The rule filters out known backup service accounts and authorized backup processes, flagging activity that occurs outside of standard business hours or is performed by accounts not explicitly designated for administrative or database maintenance tasks.
This rule detects potentially unauthorized access or large-scale extraction of sensitive RADIUS server configuration and credential databases. It monitors for common database management tools and commands used in conjunction with RADIUS-specific paths, particularly when executed by unexpected processes, during off-hours, or in a manner indicative of bulk data export.
This rule detects potentially unauthorized access or large-scale extraction of sensitive RADIUS server configuration and credential databases. It monitors for common database management tools and commands used in conjunction with RADIUS-specific paths, particularly when executed by unexpected processes, during off-hours, or in a manner indicative of bulk data export.
Detects the use of database or archiving command-line utilities to export data related to RADIUS services. The rule filters out known backup service accounts and authorized backup processes, flagging activity that occurs outside of standard business hours or is performed by accounts not explicitly designated for administrative or database maintenance tasks.
This rule detects potentially unauthorized access or large-scale extraction of sensitive RADIUS server configuration and credential databases. It monitors for common database management tools and commands used in conjunction with RADIUS-specific paths, particularly when executed by unexpected processes, during off-hours, or in a manner indicative of bulk data export.
Detects the use of database or archiving command-line utilities to export data related to RADIUS services. The rule filters out known backup service accounts and authorized backup processes, flagging activity that occurs outside of standard business hours or is performed by accounts not explicitly designated for administrative or database maintenance tasks.
Detects the use of database or archiving command-line utilities to export data related to RADIUS services. The rule filters out known backup service accounts and authorized backup processes, flagging activity that occurs outside of standard business hours or is performed by accounts not explicitly designated for administrative or database maintenance tasks.
This rule detects potentially unauthorized access or large-scale extraction of sensitive RADIUS server configuration and credential databases. It monitors for common database management tools and commands used in conjunction with RADIUS-specific paths, particularly when executed by unexpected processes, during off-hours, or in a manner indicative of bulk data export.
This rule detects potentially unauthorized access or large-scale extraction of sensitive RADIUS server configuration and credential databases. It monitors for common database management tools and commands used in conjunction with RADIUS-specific paths, particularly when executed by unexpected processes, during off-hours, or in a manner indicative of bulk data export.
The following analytic detects the use of default or publicly known named pipes associated with Cobalt Strike. It leverages Sysmon EventID 17 and 18 to identify specific named pipes commonly used by Cobalt Strike's Artifact Kit and Malleable C2 Profiles. This activity is significant because Cobalt Strike is a popular tool for adversaries to conduct post-exploitation tasks, and identifying its named pipes can reveal potential malicious activity. If confirmed malicious, this could indicate an active Cobalt Strike beacon, leading to unauthorized access, data exfiltration, or further lateral movement within the network.
This analytic detects suspicious data transfer activities involving the use of the `split` syscall with the `-b` option, which specifies the size of each output file. This technique can be used by attackers to evade detection by breaking large files into smaller parts, bypassing size-based security controls, and facilitating covert exfiltration of sensitive data. By monitoring for unusual or unauthorized use of the `split` syscall with a specified block size, this analytic helps identify potential data exfiltration attempts.
This analytic identifies the bulk creation of ransomware notes (e.g., .txt, .html, .hta files) on an infected machine. It leverages Sysmon EventCode 11 to detect multiple instances of these file types being created within a short time frame (10 seconds). A detection is triggered if 15 or more unique readme paths are observed. This activity is a strong indicator of an active ransomware attack, where the attacker is notifying the victim of the encryption. If confirmed malicious, this behavior could lead to widespread data encryption, rendering critical files inaccessible and potentially causing significant operational disruption.
The following analytic detects suspicious attempts to find credentials stored in password stores, indicating a potential attacker's effort to access sensitive login information. Password stores are critical repositories that contain valuable credentials, and unauthorized access to them can lead to significant security breaches. By monitoring for unusual or unauthorized activities related to password store access, this analytic helps identify potential credential theft attempts, allowing security teams to respond promptly and prevent unauthorized access to critical systems and data.
This analytic detects the configuration of TFTP services on Cisco IOS devices that could be used to exfiltrate sensitive configuration files. Threat actors like Static Tundra have been observed configuring TFTP servers to make device configuration files accessible for exfiltration after gaining initial access. The detection specifically looks for commands that expose critical configuration files such as startup-config, running-config, and other sensitive system information through TFTP. This activity is particularly concerning as it may represent an attempt to steal credentials, network topology information, and other sensitive data stored in device configurations.
'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)'
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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