Detection: Windows Common Abused Cmd Shell Risk Behavior

Description

The following analytic identifies instances where four or more distinct detection analytics are associated with malicious command line behavior on a specific host. This detection leverages the Command Line Interface (CLI) data from various sources to identify suspicious activities. This behavior is significant as it often indicates attempts to execute malicious commands, access sensitive data, install backdoors, or perform other nefarious actions. If confirmed malicious, attackers could gain unauthorized control, exfiltrate information, escalate privileges, or launch further attacks within the network, leading to severe compromise.

Search

1
2| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where source IN ("*Windows Cmdline Tool Execution From Non-Shell Process*", "*Windows System Network Config Discovery Display DNS*", "*Local Account Discovery With Wmic*", "*Windows Group Discovery Via Net*", "*Windows Create Local Administrator Account Via Net*", "*Windows User Discovery Via Net*", "*Icacls Deny Command*", "*ICACLS Grant Command*", "*Windows Proxy Via Netsh*", "*Processes launching netsh*", "*Disabling Firewall with Netsh*", "*Windows System Network Connections Discovery Netsh*", "*Network Connection Discovery With Arp*", "*Windows System Discovery Using ldap Nslookup*", "*Windows System Shutdown CommandLine*") by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic 
3| `drop_dm_object_name(All_Risk)` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| where source_count >= 4 
7| `windows_common_abused_cmd_shell_risk_behavior_filter`

Data Source

No data sources specified for this detection.

Macros Used

Annotations

Default Configuration

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

Implementation

Splunk Enterprise Security is required to utilize this correlation. In addition, modify the source_count value to your environment. In our testing, a count of 4 or 5 was decent in a lab, but the number may need to be increased base on internal testing. In addition, based on false positives, modify any analytics to be anomaly and lower or increase risk based on organization importance.

Known False Positives

False positives will be present based on many factors. Tune the correlation as needed to reduce too many triggers.

Associated Analytic Story

• Azorult

• CISA AA23-347A

• DarkCrystal RAT

• Disabling Security Tools

• FIN7

• Netsh Abuse

• Qakbot

• Sandworm Tools

• Volt Typhoon

• Windows Defense Evasion Tactics

• Windows Post-Exploitation

References

• https://www.splunk.com/en_us/blog/security/from-macros-to-no-macros-continuous-malware-improvements-by-qakbot.html

• https://www.splunk.com/en_us/blog/security/dark-crystal-rat-agent-deep-dive.html

Detection Testing

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: 4