Detection: Detect Remote Access Software Usage Traffic

Description

The following analytic detects network traffic associated with known remote access software applications, such as AnyDesk, GoToMyPC, LogMeIn, and TeamViewer. It leverages Palo Alto traffic logs mapped to the Network_Traffic data model in Splunk. This activity is significant because adversaries often use remote access tools to maintain unauthorized access to compromised environments. If confirmed malicious, this activity could allow attackers to control systems remotely, exfiltrate data, or deploy additional malware, posing a severe threat to the organization's security.

1
2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Traffic.dest_port) as dest_port latest(user) as user from datamodel=Network_Traffic by All_Traffic.src All_Traffic.dest, All_Traffic.app 
3| `drop_dm_object_name("All_Traffic")` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| lookup remote_access_software remote_appid AS app OUTPUT isutility, description as signature, comment_reference as desc, category 
7| search isutility = True 
8| `remote_access_software_usage_exceptions` 
9| `detect_remote_access_software_usage_traffic_filter`

Data Source

Name Platform Sourcetype Source Supported App
Palo Alto Network Traffic Network icon Network 'pan:traffic' 'screenconnect_palo_traffic' N/A

Macros Used

Name Value
remote_access_software_usage_exceptions `eval exception_asset = CASE(isnotnull(src),src,isnotnull(dest),dest)
detect_remote_access_software_usage_traffic_filter search *
detect_remote_access_software_usage_traffic_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
T1219 Remote Access Software Command And Control
KillChainPhase.COMMAND_AND_CONTROL
NistCategory.DE_AE
Cis18Value.CIS_13
Akira
Carbanak
Cobalt Group
DarkVishnya
Evilnum
FIN7
GOLD SOUTHFIELD
Kimsuky
MuddyWater
Mustang Panda
RTM
Sandworm Team
Scattered Spider
TeamTNT
Thrip

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 following analytic was developed with Palo Alto traffic logs. Ensure that the logs are being ingested into Splunk and mapped to the Network_Traffic data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. The "exceptions" macro leverages both an Assets and Identities lookup, as well as a KVStore collection called "remote_software_exceptions" that lets you track and maintain device- based exceptions for this set of detections.

Known False Positives

It is possible that legitimate remote access software is used within the environment. Ensure that the lookup is reviewed and updated with any additional remote access software that is used within the environment. Known false positives can be added to the remote_access_software_usage_exception.csv lookup to globally suppress these situations across all remote access content

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
Application traffic for a known remote access software [$signature$] was detected from $src$. 25 50 50
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 screenconnect_palo_traffic pan:traffic
Integration ✅ Passing Dataset screenconnect_palo_traffic pan:traffic

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