Unusual Linux Network Activityedit
Identifies Linux processes that do not usually use the network but have unexpected network activity, which can indicate command-and-control, lateral movement, persistence, or data exfiltration activity. A process with unusual network activity can denote process exploitation or injection, where the process is used to run persistence mechanisms that allow a malicious actor remote access or control of the host, data exfiltration, and execution of unauthorized network applications.
Rule type: machine_learning
Machine learning job: linux_anomalous_network_activity_ecs
Machine learning anomaly threshold: 50
Severity: low
Risk score: 21
Runs every: 15 minutes
Searches indices from: now-45m (Date Math format, see also Additional look-back time
)
Maximum signals per execution: 100
References:
Tags:
- Elastic
- Linux
- ML
Version: 1
Added (Elastic Stack release): 7.7.0
Potential false positivesedit
A newly installed program or one that rarely uses the network could trigger this signal.
Investigation guideedit
Signals from this rule indicate the presence of network activity from a Linux process for which network activity is rare and unusual. Here are some possible avenues of investigation:
- Consider the IP addresses and ports. Are these used by normal but infrequent network workflows? Are they expected or unexpected?
-
If the destination IP address is remote or external, does it associate with an expected domain, organization or geography?
Avoid interacting directly with suspected malicious IP addresses.
- Consider the user as identified by the username field. Is this network activity part of an expected workflow for the user who ran the program?
- Examine the history of execution. If this process manifested only very recently, it might be part of a new software package. If it has a consistent schedule - for example if it runs monthly or quarterly - it might be part of a monthly or quarterly business or maintenance process.
- Examine the process arguments, title and working directory. These may provide indications as to the source of the program or the nature of the tasks it is performing.