Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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The analytic detects potential data exfiltration using plain HTTP POST requests. It monitors network traffic logs, specifically the `stream_http` data source, for POST methods containing suspicious form data such as "wermgr.exe", "svchost.exe", "name=\"proclist\"", "ipconfig", "name=\"sysinfo\"", or "net view". This activity is commonly associated with malware like Trickbot, trojans, keyloggers, or APT adversaries, which use plain text HTTP POST requests to communicate with remote C2 servers. If confirmed malicious, this activity could lead to unauthorized data exfiltration, compromising sensitive information and potentially leading to further network infiltration.
The analytic detects the use of Windows Curl.exe to upload a file to a remote destination. It identifies command-line arguments such as `-T`, `--upload-file`, `-d`, `--data`, and `-F` in process execution logs. This activity is significant because adversaries may use Curl to exfiltrate data or upload malicious payloads. If confirmed malicious, this could lead to data breaches or further compromise of the system. Analysts should review parallel processes and network logs to determine if the upload was successful and isolate the endpoint if necessary.
This rule detects suspicious file write and network activity that may indicate data exfiltration from a Windows Server using common tools like curl, ftp, or powershell. It correlates file creation/modification events with outbound network connections made by known exfiltration tools within a 5-minute window. This is useful for identifying insider threats, compromised servers, and automated exfiltration scripts.
Detects network traffic to an ERMAC banking trojan exfiltration server by identifying a unique 'WWW-Authenticate' header in the HTTP response. This indicates a client device is communicating with a known ERMAC command and control server for data exfiltration.
This rule detects suspicious activity where scripting interpreters (node.exe, python.exe, pwsh.exe, powershell.exe) are observed accessing a large number of potentially sensitive files (e.g., .env, secret, wallet, keypair, credential, recovery files, or common document/data file types like .pdf, .docx, .csv, .json, .txt) and subsequently exfiltrating a significant amount of data over the network. It correlates file access events with network outbound traffic from the same process within a 10-minute window, flagging instances where more than 20 distinct sensitive files are accessed and over 500KB of data is sent out.
This rule detects potential data exfiltration attempts using the built-in Windows `ping.exe` utility. Specifically, it looks for `ping.exe` executions where a large payload size (greater than 1024 bytes) is specified using the -L command-line argument. This technique can be used to tunnel data over ICMP, which might bypass some network defenses.
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 detection identifies outbound NTLM authentication attempts to remote SMB servers with suspicious characteristics consistent with the use of impacket-smbserver, a tool commonly used by adversaries for lateral movement and data exfiltration. The detection looks for RemotePort 445 traffic where:
- The server version is the anomalous string 255.255 65535 255
- Server identity fields (DNS and NetBIOS names) are exactly 8 characters long, which is typical for randomly generated names by impacket-smbserver
This pattern strongly indicates impersonation or malicious SMB hosting by an attacker within the network.
- The server version is the anomalous string 255.255 65535 255
- Server identity fields (DNS and NetBIOS names) are exactly 8 characters long, which is typical for randomly generated names by impacket-smbserver
This pattern strongly indicates impersonation or malicious SMB hosting by an attacker within the network.
This query identifies devices with frequent outbound HTTP/HTTPS connections to external IPs, suggesting possible chunked data exfiltration.
KQL Query from file: Data Exfiltration via Microsoft Teams
KQL Query from file: Blob URI Unique Domain Count
The following analytic identifies anomalous network traffic volumes between Kubernetes workloads or between a workload and external sources. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares recent network metrics (tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets) over the last hour with the average over the past 30 days to identify significant deviations. This activity is significant as unexpected spikes may indicate unauthorized data transfers or lateral movement. If confirmed malicious, it could lead to data exfiltration or compromise of additional services, potentially resulting in data breaches.
The analytic identifies anomalous inbound network traffic volumes from processes within containerized workloads. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares recent metrics (tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets) over the last hour with the average over the past 30 days. This activity is significant as it may indicate unauthorized data reception, potential breaches, vulnerability exploitation, or malware propagation. If confirmed malicious, it could lead to command and control installation, data integrity damage, container escape, and further environment compromise.
'Identifies anomalous data transfer to public networks. The query leverages built-in KQL anomaly detection algorithms that detects large deviations from a baseline pattern.
A sudden increase in data transferred to unknown public networks is an indication of data exfiltration attempts and should be investigated.
The higher the score, the further it is from the baseline value.
The output is aggregated to provide summary view of unique source IP to destination IP address and port bytes sent traffic observed in the flagged anomaly hour.
The source IP addresses which were sending less than bytessentperhourthreshold have been exluded whose value can be adjusted as needed .
You may have to run queries for individual source IP addresses from SourceIPlist to determine if anything looks suspicious'
A sudden increase in data transferred to unknown public networks is an indication of data exfiltration attempts and should be investigated.
The higher the score, the further it is from the baseline value.
The output is aggregated to provide summary view of unique source IP to destination IP address and port bytes sent traffic observed in the flagged anomaly hour.
The source IP addresses which were sending less than bytessentperhourthreshold have been exluded whose value can be adjusted as needed .
You may have to run queries for individual source IP addresses from SourceIPlist to determine if anything looks suspicious'
'Identifies anamalous spikes in network traffic logs as compared to baseline or normal historical patterns.
The query leverages a KQL built-in anomaly detection algorithm to find large deviations from baseline patterns.
Sudden increases in network traffic volume may be an indication of data exfiltration attempts and should be investigated.
The higher the score, the further it is from the baseline value.
The output is aggregated to provide summary view of unique source IP to destination IP address and port traffic observed in the flagged anomaly hour.
The source IP addresses which were sending less than percentotalthreshold of the total traffic have been exluded whose value can be adjusted as needed .
You may have to run queries for individual source IP addresses from SourceIPlist to determine if anything looks suspicious'
The query leverages a KQL built-in anomaly detection algorithm to find large deviations from baseline patterns.
Sudden increases in network traffic volume may be an indication of data exfiltration attempts and should be investigated.
The higher the score, the further it is from the baseline value.
The output is aggregated to provide summary view of unique source IP to destination IP address and port traffic observed in the flagged anomaly hour.
The source IP addresses which were sending less than percentotalthreshold of the total traffic have been exluded whose value can be adjusted as needed .
You may have to run queries for individual source IP addresses from SourceIPlist to determine if anything looks suspicious'
'This query helps identify rare DNS connections and resulting data transfer to/from the associated domain. It can help identify unexpected large data transfers to or from internal systems which may indicate data exfil or malicious tool download.'
Background Intelligent Transfer Service (BITS) is a way to reliably download files from webservers or SMB servers.
This service is commonly used for legitimate purposes, but can also be used as part of a malware downloader.
Additionally, bitsadmin can be used to upload files and therefore can be used for data exfiltration. This
query will identify use of bitsadmin.exe for either purpose and will identify directionality file transfer
directionality.
This service is commonly used for legitimate purposes, but can also be used as part of a malware downloader.
Additionally, bitsadmin can be used to upload files and therefore can be used for data exfiltration. This
query will identify use of bitsadmin.exe for either purpose and will identify directionality file transfer
directionality.
Intent:
- Use MTP capability to look for insider threat potential risk indicators
- Indicators would then serve as the building block for insider threat risk modeling in subsequent tools
Definition of Insider Threat:
"The potential for an individual who has or had authorized access to an organization's assets to use their access, either maliciously or unintentionally, to act in a way that could negatively affect the organization."
This collection of queries describes the different indicators that could be used to model and look for patterns suggesting an increased risk of an individual becoming a potential insider threat.
Note: no single indicator should be used as a lone determinant of insider threat activity, but should be part of an overall program to understand the increased risk to your organization's critical assets. This in turn is used to feed an investigation by a formal insider threat program to look at the context associated with the whole person to understand the implication of a set of indicators.
- Use MTP capability to look for insider threat potential risk indicators
- Indicators would then serve as the building block for insider threat risk modeling in subsequent tools
Definition of Insider Threat:
"The potential for an individual who has or had authorized access to an organization's assets to use their access, either maliciously or unintentionally, to act in a way that could negatively affect the organization."
This collection of queries describes the different indicators that could be used to model and look for patterns suggesting an increased risk of an individual becoming a potential insider threat.
Note: no single indicator should be used as a lone determinant of insider threat activity, but should be part of an overall program to understand the increased risk to your organization's critical assets. This in turn is used to feed an investigation by a formal insider threat program to look at the context associated with the whole person to understand the implication of a set of indicators.
'This hunting query looks for users whose total returned data that is significantly above their average.'
Detects the use of command-line tools such as 'scp', 'rsync', and 'sftp' that are commonly used to copy files to or from remote systems. This activity can be indicative of an adversary transferring tools into an environment (Ingress Tool Transfer) or moving files laterally within a compromised network (Lateral Tool Transfer). The rule specifically looks for the presence of these tool names followed by a space and then a colon or an '@' symbol, which are characteristic of remote file operations.
Remote File Copy
Sigma
Detects the use of tools that copy files from or to remote systems
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