Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
53 detections
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This rule analyzes historical logon, process, and network activity for a specified device over a configurable lookback period. It aims to validate whether a device, potentially flagged as decommissioned or stale, exhibits a pattern of sporadic, legitimate use (e.g., executive laptops, break-glass machines) rather than continuous or unauthorized activity. The output provides a daily timeline of activity, helping to differentiate between truly dormant assets and those with intermittent, legitimate usage.
Detects the "EtherHiding" technique used by the FakeGit campaign. The malware queries a Polygon Mainnet smart contract via a public RPC node to resolve its current C2 server address. This rule looks for network connections to known blockchain RPC providers containing the specific function selectors used by the malware's V1 and V2 contracts.
This detection identifies outbound network traffic to domains, IP addresses, or a specific port known to be associated with the PHALT#BLYX campaign's infrastructure. The campaign uses these for malware delivery and DCRat command and control (C2). The rule specifically looks for connections to known malicious IPs or domains, or connections on port 3535 (used by DCRat) originating from processes associated with the malware (staxs.exe, tybd7.exe, aspnet_compiler.exe).
This rule detects the usage of cleartext protocols for transmitting sensitive information, such as account usernames and authentication credentials, across networks. It specifically monitors Netflow data for connections to common ports associated with unencrypted services like HTTP, FTP, Telnet, and various database/application ports. The goal is to ensure that all sensitive data and administrative access utilize encrypted channels to prevent credential access and data interception.
This KQL Query is oriented to extract the different bits of TCP Flags which helps to review the different state of the connections and identify possible threats associated.
In addition, it allows to filter by specific TCP Flag to detect specific threats based on every case.
In addition, it allows to filter by specific TCP Flag to detect specific threats based on every case.
This rule detects successful network connections from external (internet) IP addresses to common Industrial Control System (ICS) ports, such as Modbus (502) and EtherNet/IP (44818). This activity could indicate unauthorized access attempts, reconnaissance, or exploitation of internet-exposed ICS assets. The rule specifically filters for allowed/accepted/permitted connections and excludes connections from RFC 1918 private IP addresses and a configurable list of authorized external IPs.
The analytic identifies potential Remote Desktop Protocol (RDP) brute force attacks by monitoring network traffic for RDP application activity. It detects potential RDP brute force attacks by identifying source IPs that have made more than 10 connection attempts to the same RDP port on a host within a one-hour window. The results are presented in a table that includes the source and destination IPs, destination port, number of attempts, and the times of the first and last connection attempts, helping to prioritize IPs based on the intensity of activity.
This detection identifies network traffic to or from IP addresses known to be associated with command and control (C2) infrastructure or active exploitation in recent SharePoint vulnerability campaigns. Communication with these IPs is a strong indicator of either an attempted attack or an existing compromise.
KQL Query
KQL Query from file: DefenderXDR MITRE ATT&CK Technique Analysis
This analytic identifies anomalously high outbound network activity from processes within Kubernetes containerized workloads. It compares recent network metrics (TCP/UDP bytes, new sockets, packets) over the last hour against the average metrics from the past 30 days. Significant deviations may indicate data exfiltration, process modification, or container compromise, potentially leading to unauthorized data transfer or further attacks within the containerized environment.
This analytic detects new UDP communication between a source and destination workload pair within a Kubernetes cluster. It uses Network Performance Monitoring (NPM) metrics from an OTEL collector via Splunk Observability Cloud. The detection compares network activity over the last hour with the past 30 days to identify new inter-workload communication. Such changes in network behavior can indicate potential security threats or anomalies, including unauthorized connections that may lead to data breaches, privilege escalation, lateral movement, or disruption of critical services.
This analytic identifies newly observed TCP communication between source and destination workloads within a Kubernetes cluster. It leverages Network Performance Monitoring metrics to compare recent network activity with historical data (past 30 days) to detect new inter-workload communications. Such new connections can indicate changes in application behavior or potential security threats, including unauthorized access, lateral movement, privilege escalation, data exfiltration, or disruption of critical services.
This query searches for a string pattern detected in evasive PowerShell usage. Jupyter or SolarMarker will iterate on this pattern multiple times to read data and call additional processes. This query is not fully specific to Jupyter or SolarMarker, and will also return other malicious malware, but is unlikely to return false positives.
'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 checks for network connection failures to Microsoft Defender for Endpoint URLs.
The output includes any device with 1+ connectivity failures, a list of the domains they
failed to connect to (including the number of failures), as well as the overall number of
failures in the time period. Results are sorted by the total number of connection failures
by the device.
The output includes any device with 1+ connectivity failures, a list of the domains they
failed to connect to (including the number of failures), as well as the overall number of
failures in the time period. Results are sorted by the total number of connection failures
by the device.
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.
Get all filtering events done by the Windows filtering platform.
This includes any blocks done by Windows Firewall rules, but also blocks triggered by some 3rd party firewalls.
When no Firewall rules are configured, the default behavior is to block all incoming network connections.
This includes any blocks done by Windows Firewall rules, but also blocks triggered by some 3rd party firewalls.
When no Firewall rules are configured, the default behavior is to block all incoming network connections.
The following query surface network activity associated with exploitation of CVE-2022-22965.
Simple query to show the unique network connections that were audited or blocked by ExploitGuard.
For more questions on this query, feel free to ping @FlyingBlueMonki on twitter or mattegen@microsoft.com via email.
For more questions on this query, feel free to ping @FlyingBlueMonki on twitter or mattegen@microsoft.com via email.
The following queries pivot from an IP address assigned to a machine to the relevant machine or logged-on users.
To read more about it, check out this post: https://techcommunity.microsoft.com/t5/What-s-New/Advanced-hunting-now-includes-network-adapters-information/m-p/224402#M74.
Query #1: get machines that have used a given local IP address at a given time - as configured on their network adapters.
To read more about it, check out this post: https://techcommunity.microsoft.com/t5/What-s-New/Advanced-hunting-now-includes-network-adapters-information/m-p/224402#M74.
Query #1: get machines that have used a given local IP address at a given time - as configured on their network adapters.
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