Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects initial execution patterns and stealer activities used by KimJongRAT PE and PowerShell variants, including LNK/HTA execution, certutil decoding, rundll32 execution, and PowerShell-based data collection.
KQL Query from file: DefenderXDR Weekly OSINT Indicators Scan 05052025
KQL Query from file: Golden Chickens TerraStealerV2 Malware Detection
KQL Query
KQL Query from file: Blob URI Unique Domain Count
KQL Query from file: Hunting VEILDrive C2
This YARA rule detects the ChaosRAT backdoor by identifying specific byte sequences related to its network communication, system information gathering, and file download capabilities. The rule includes three distinct versions of these functionalities, indicating evolution or different variants of the malware. Detection relies on the presence of these unique byte patterns within executable files, specifically ELF files as indicated by the magic byte check.
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 PowerShell implementation of DNSCat2 calls nslookup to craft queries. Counting nslookup processes spawned by PowerShell will show hundreds or thousands of instances if PS DNSCat2 is active locally.
'Identifies patterns in the time deltas of contacts between internal and external IPs in Fortinet network data that are consistent with beaconing.
Accounts for randomness (jitter) and seasonality such as working hours that may have been introduced into the beacon pattern.
The lookback is set to 1d, the minimum granularity in time deltas is set to 60 seconds and the minimum number of beacons required to emit a detection is set to 4.
Increase the lookback period to capture beacons with larger periodicities.
The jitter tolerance is set to 0.2 - This means we account for an overall 20% deviation from the infered beacon periodicity. Seasonality is dealt with automatically using series_outliers.
Note: In large environments it may be necessary to reduce the lookback period to get fast query times.'
Accounts for randomness (jitter) and seasonality such as working hours that may have been introduced into the beacon pattern.
The lookback is set to 1d, the minimum granularity in time deltas is set to 60 seconds and the minimum number of beacons required to emit a detection is set to 4.
Increase the lookback period to capture beacons with larger periodicities.
The jitter tolerance is set to 0.2 - This means we account for an overall 20% deviation from the infered beacon periodicity. Seasonality is dealt with automatically using series_outliers.
Note: In large environments it may be necessary to reduce the lookback period to get fast query times.'
'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.'
'Query identifies beaconing patterns from Wire Data logs. Uses KQL functions to calculate time delta and find beaconing percentage. Results of beaconing to untrusted public networks can be investigated.'
This query can be used to explore any instances where a terminated individual (i.e. one who has an impending termination date but has not left the company) downloads a large number of files from a non-Domain network address.
'This detection will identify network requests in HTTP proxy data that contains Base64 encoded IP addresses. After identifying candidates the query joins with DeviceNetworkEvents to idnetify any machine within the network using that IP address. Alerts indicate that the IP address of a machine within your network was seen with it's IP address base64 encoded in an outbound web request. This method of egressing the IP was seen used in POLONIUM's RunningRAT tool, however the detection is generic.'
'This query looks for command lines that contain a public IP address. Attackers may use a hard coded IP for C2 or exfiltration.
This query can be filtered to exclude network prefixes that are known to be legitimate.'
This query can be filtered to exclude network prefixes that are known to be legitimate.'
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 query was originally published in the threat analytics report, MailSniper Exchange attack tool.
MailSniper is a tool that targets Microsoft Exchange Server. The core function is to connect to Exchange Server and search through emails. In support of this, it can perform reconnaissance, collection, exfiltration, and credential theft. MailSniper is used both by red teams running penetration tests, and by malicious actors.
Microsoft Defender Security Center may record the following alerts during and after an attack:
1. Global mail search on Exchange using MailSniper
2. Exchange mailbox or mail folder search using MailSniper
3. Enumeration of Active Directory usernames using MailSniper
4. Enumeration of the Exchange GAL using MailSniper
5. Access to Exchange inboxes using MailSniper
6. Password spraying using MailSniper
7. Enumeration of domains and user accounts using MailSniper
The following query detects activity commonly associated with attacks run with MailSniper.
Reference - https://github.com/dafthack/MailSniper
MailSniper is a tool that targets Microsoft Exchange Server. The core function is to connect to Exchange Server and search through emails. In support of this, it can perform reconnaissance, collection, exfiltration, and credential theft. MailSniper is used both by red teams running penetration tests, and by malicious actors.
Microsoft Defender Security Center may record the following alerts during and after an attack:
1. Global mail search on Exchange using MailSniper
2. Exchange mailbox or mail folder search using MailSniper
3. Enumeration of Active Directory usernames using MailSniper
4. Enumeration of the Exchange GAL using MailSniper
5. Access to Exchange inboxes using MailSniper
6. Password spraying using MailSniper
7. Enumeration of domains and user accounts using MailSniper
The following query detects activity commonly associated with attacks run with MailSniper.
Reference - https://github.com/dafthack/MailSniper
'This detection will alert when RunningRAT URI parameters or paths are detect in an HTTP request.
Id the device blocked this communication presence of this alert means the RunningRAT implant is likely still executing on the source host.'
Id the device blocked this communication presence of this alert means the RunningRAT implant is likely still executing on the source host.'
LemonDuck is an actively updated and robust malware primarily known for its botnet and cryptocurrency mining objectives. First discovered in 2019, LemonDuck has since adopted more sophisticated behavior and escalated its operations in 2021. Today, beyond using resources for its traditional bot and mining activities, LemonDuck steals credentials, removes security controls, spreads via emails, moves laterally, and ultimately drops more tools for human-operated activity.
Query 1 shows you any network communication happened from endpoints to a specific Remote IP or Remote URL.
Ensure to update RemoteIP and RemoteURL variable.
For questions @MiladMSFT on Twitter or milad.aslaner@microsoft.com by email.
Ensure to update RemoteIP and RemoteURL variable.
For questions @MiladMSFT on Twitter or milad.aslaner@microsoft.com by email.
Detects the execution of well known tools that can be abused for data exfiltration and tunneling.
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