Detection: Windows Apache Benchmark Binary

Updated Date: 2024-08-14 ID: 894f48ea-8d85-4dcd-9132-c66cdb407c9b Author: Michael Haag, Splunk Type: Anomaly Product: Splunk Enterprise Security

Description

The following analytic detects the execution of the Apache Benchmark binary (ab.exe), commonly used by MetaSploit payloads. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events where the original file name is ab.exe. This activity is significant as it may indicate the presence of a MetaSploit attack, which uses Apache Benchmark to generate malicious payloads. If confirmed malicious, this could lead to unauthorized network connections, further system compromise, and potential data exfiltration. Immediate investigation is required to determine the intent and scope of the activity.

1
2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.original_file_name=ab.exe by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id 
3| `drop_dm_object_name(Processes)` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| `windows_apache_benchmark_binary_filter`

Data Source

Name Platform Sourcetype Source Supported App
CrowdStrike ProcessRollup2 N/A 'crowdstrike:events:sensor' 'crowdstrike' N/A

Macros Used

Name Value
security_content_ctime convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)
windows_apache_benchmark_binary_filter search *
windows_apache_benchmark_binary_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Annotations

- MITRE ATT&CK
+ Kill Chain Phases
+ NIST
+ CIS
- Threat Actors
ID Technique Tactic
T1059 Command and Scripting Interpreter Execution
KillChainPhase.INSTALLATION
NistCategory.DE_AE
Cis18Value.CIS_10
APT19
APT32
APT37
APT39
Dragonfly
FIN5
FIN6
FIN7
Fox Kitten
Ke3chang
OilRig
Stealth Falcon
Whitefly
Windigo

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Setting Value
Disabled true
Cron Schedule 0 * * * *
Earliest Time -70m@m
Latest Time -10m@m
Schedule Window auto
Creates Risk Event True
This configuration file applies to all detections of type anomaly. These detections will use Risk Based Alerting.

Implementation

The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the Processes node of the Endpoint data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.

Known False Positives

False positives should be limited as there is a small subset of binaries that contain the original file name of ab.exe. Filter as needed.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
A known MetaSploit default payload has been identified on $dest$ ran by $user$, $parent_process_name$ spawning $process_name$. 100 100 100
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

References

Detection Testing

Test Type Status Dataset Source Sourcetype
Validation ✅ Passing N/A N/A N/A
Unit ✅ Passing Dataset XmlWinEventLog:Microsoft-Windows-Sysmon/Operational xmlwineventlog
Integration ✅ Passing Dataset XmlWinEventLog:Microsoft-Windows-Sysmon/Operational xmlwineventlog

Replay any dataset to Splunk Enterprise by using our replay.py tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

Source: GitHub | Version: 3