Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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The analytic identifies Domain Generation Algorithm (DGA) generated domains using a pre-trained deep learning model. It leverages network resolution data to analyze domain names and detect unusual character sequences indicative of DGA activity. Adversaries often use DGAs to generate numerous domain names for command-and-control servers, making it harder to block malicious traffic. If confirmed malicious, this activity could enable attackers to maintain persistent communication with compromised systems, evade detection, and execute further malicious actions.
The following analytic detects the use of the dnscmd.exe command to enumerate DNS records. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line executions. This activity is significant as it may indicate an adversary gathering network information, a common precursor to more targeted attacks. If confirmed malicious, this behavior could enable attackers to map the network, identify critical assets, and plan subsequent actions, potentially leading to data exfiltration or further compromise of the network.
The following analytic identifies suspicious Office documents that connect to non-Microsoft Office domains. It leverages Sysmon EventCode 22 to detect processes like winword.exe or excel.exe making DNS queries to domains outside of *.office.com or *.office.net. This activity is significant as it may indicate a spearphishing attempt using malicious documents to download or connect to harmful content. If confirmed malicious, this could lead to unauthorized data access, malware infection, or further network compromise.
The analytic identifies the loading of the malicious SolarWinds.Orion.Core.BusinessLayer.dll by SolarWinds.BusinessLayerHost.exe and subsequent DNS queries to avsvmcloud.com. It uses Sysmon EventID 7 for DLL loading and Event ID 22 for DNS queries, correlating these events within a 12-14 day period. This activity is significant as it indicates potential Sunburst malware infection, a known supply chain attack. If confirmed malicious, this could lead to unauthorized network access, data exfiltration, and further compromise of the affected systems.
This detection identifies DNS queries to domains that match previously decommissioned S3 buckets. This activity is significant because attackers may attempt to recreate deleted S3 buckets that were previously public to hijack them for malicious purposes. If successful, this could allow attackers to host malicious content or exfiltrate data through compromised bucket names that may still be referenced by legitimate applications.
The analytic detects attempts to exploit the SIGRed vulnerability (CVE-2020-1350) in Windows DNS servers. It leverages Splunk Stream DNS and TCP data to identify DNS SIG and KEY records, as well as TCP payloads exceeding 65KB. This activity is significant because SIGRed is a critical wormable vulnerability that allows remote code execution. If confirmed malicious, an attacker could gain unauthorized access, execute arbitrary code, and potentially disrupt services, leading to severe data breaches and infrastructure compromise. Immediate investigation and remediation are crucial to mitigate these risks.
The following analytic identifies DNS requests with unusually large query lengths for the record type being requested. It leverages the Network_Resolution data model and applies a machine learning model to detect outliers in DNS query lengths. This activity is significant because unusually large DNS queries can indicate data exfiltration or command-and-control communication attempts. If confirmed malicious, this activity could allow attackers to exfiltrate sensitive data or maintain persistent communication channels with compromised systems.
This analytic identifies a process making a DNS query to Discord, excluding legitimate Discord application paths. It leverages Sysmon logs with Event ID 22 to detect DNS queries containing "discord" in the QueryName field. This activity is significant because Discord can be abused by adversaries to host and download malicious files, as seen in the WhisperGate campaign. If confirmed malicious, this could indicate malware attempting to download additional payloads from Discord, potentially leading to further code execution and compromise of the affected system.
This analytic identifies DNS queries from internal hosts to dynamic domain providers. It leverages DNS query logs from the `Network_Resolution` data model and cross-references them with a lookup file containing known dynamic DNS providers. This activity is significant because attackers often use dynamic DNS services to host malicious payloads or command-and-control servers, making it crucial for security teams to monitor. If confirmed malicious, this activity could allow attackers to bypass firewall blocks, evade detection, and maintain persistent access to the network.
This analytic identifies DNS queries with unusually large lengths by computing the standard deviation of query lengths and filtering those exceeding twice the standard deviation. It leverages DNS query data from the Network_Resolution data model, focusing on the length of the domain names being resolved. This activity is significant as unusually long DNS queries can indicate data exfiltration or command-and-control communication attempts. If confirmed malicious, this activity could allow attackers to stealthily transfer data or maintain persistent communication channels within the network.
This analytic detects suspicious `rundll32.exe` processes making DNS queries. This behavior is often associated with malware like IcedID, which uses `rundll32.exe` to check internet connectivity and communicate with command and control (C2) servers for configuration and payload downloads. Successful exploitation could lead to persistence, additional payload delivery, and data exfiltration.
The analytic identifies the execution of the "ipconfig /displaydns" command, which retrieves DNS reply information using the built-in Windows tool IPConfig. Monitoring this activity is significant as threat actors and post-exploitation tools like WINPEAS often abuse this command to gather network information. If confirmed malicious, this activity could allow attackers to map the network, identify DNS servers, and potentially facilitate further network-based attacks or lateral movement.
This rule detects DNS queries to domains that have been created within the last 30 days. This behavior can be indicative of newly registered malicious domains used for command and control, phishing, or other adversarial activities. The rule correlates DNS query events with WHOIS data to identify the domain creation time.
This rule detects DNS queries for domains that contain a specific string (e.g., a company name) and are not registered with an approved domain registrar. It leverages WHOIS data to identify potentially malicious typosquatting domains that have been recently registered or observed. The rule specifically looks for DNS queries where the domain name matches a defined pattern (e.g., 'threatpunter') and the WHOIS information indicates a registrar other than the approved one ('MarkMonitor Inc.'). Additionally, it checks if the domain was first seen in the environment within the last 30 days, indicating a new or suspicious registration.
This rule detects when Amazon GuardDuty identifies an Amazon EC2 instance or a container querying algorithmically generated domains (DGA). DGA activity is often indicative of malware attempting to establish command and control (C2) communication with its operators. The rule specifically looks for GuardDuty findings with the product event type 'DGADomainRequest' and filters out sample findings.
This rule detects DNS queries for domains that have a very low rolling maximum prevalence (3 or less over a 10-day period). This can indicate newly registered domains, domains used for command and control, or other suspicious activity that has not yet gained widespread legitimate use. The rule correlates DNS events with derived prevalence data to identify these low-prevalence domains.
Domain Prevalence
YARA-L
Detects DNS events querying domains that have a low rolling max prevalence.
DNS query to a recently created domain
Amazon GuardDuty detects an Amazon EC2 instance or a container querying algorithmically generated domains (DGA).
Provides example usage of WHOIS data, detecting a DNS query for a domain that contains a specific string and is not registered with the defined domain registrar.
Detects DNS queries for specific command and control (C2) domains known to be used by DPRK (Democratic People's Republic of Korea) threat actors. The rule specifically looks for 'connection.lockscreen.kro.kr' and 'updating.dothome.co.kr' in DNS query names.
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