Excessive Usage of NSLOOKUP App
This search is to detect potential DNS exfiltration using nslookup application. This technique are seen in couple of malware and APT group to exfiltrated collected data in a infected machine or infected network. This detection is looking for unique use of nslookup where it tries to use specific record type (TXT, A, AAAA) that are commonly used by attacker and also the retry parameter which is designed to query C2 DNS multiple tries.
- Type: Anomaly
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2022-06-03
- Author: Teoderick Contreras, Stanislav Miskovic, Splunk
- ID: 0a69fdaa-a2b8-11eb-b16d-acde48001122
Kill Chain Phase
1 2 3 4 5 6 7 8 9 10 `sysmon` EventCode = 1 process_name = "nslookup.exe" | bucket _time span=1m | stats count as numNsLookup by Computer, _time | eventstats avg(numNsLookup) as avgNsLookup, stdev(numNsLookup) as stdNsLookup, count as numSlots by Computer | eval upperThreshold=(avgNsLookup + stdNsLookup *3) | eval isOutlier=if(numNsLookup > 20 and numNsLookup >= upperThreshold, 1, 0) | search isOutlier=1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_usage_of_nslookup_app_filter`
The SPL above uses the following Macros:
excessive_usage_of_nslookup_app_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
List of fields required to use this analytic.
How To Implement
To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of nslookup.exe may be used.
Known False Positives
Associated Analytic Story
|28.0||40||70||Excessive usage of nslookup.exe has been detected on $Computer$. This detection is triggered as as it violates the dynamic threshold|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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source | version: 2