SMB Traffic Spike
THIS IS A EXPERIMENTAL DETECTION
This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.
Description
The following analytic detects spikes in the number of Server Message Block (SMB) traffic connections. SMB is a network protocol used for sharing files, printers, and other resources between computers. This detection is made by a Splunk query that looks for SMB traffic connections on ports 139 and 445, as well as connections using the SMB application. The query calculates the average and standard deviation of the number of SMB connections over the past 70 minutes, and identifies any sources that exceed two standard deviations from the average. This helps to filter out false positives caused by normal fluctuations in SMB traffic. This detection is important because it identifies potential SMB-based attacks, such as ransomware or data theft, which often involve a large number of SMB connections. This suggests that an attacker is attempting to exfiltrate data or spread malware within the network. Next steps include investigating the source of the traffic and determining if it is malicious. This can involve reviewing network logs, capturing and analyzing any relevant network packets, and correlating with other security events to identify the attack source and mitigate the risk.
- Type: Anomaly
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Network_Traffic
- Last Updated: 2020-07-22
- Author: David Dorsey, Splunk
- ID: 7f5fb3e1-4209-4914-90db-0ec21b936378
Annotations
ATT&CK
Kill Chain Phase
- Exploitation
NIST
- DE.AE
CIS20
- CIS 13
CVE
Search
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| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src
| `drop_dm_object_name("All_Traffic")`
| eventstats max(_time) as maxtime
| stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time<relative_time(maxtime, "-70m@m"), count, null))) as avg stdev(eval(if(_time<relative_time(maxtime, "-70m@m"), count, null))) as stdev by src
| eval upperBound=(avg+stdev*2), isOutlier=if(count > upperBound AND num_data_samples >=50, 1, 0)
| where isOutlier=1
| table src count
| `smb_traffic_spike_filter`
Macros
The SPL above uses the following Macros:
smb_traffic_spike_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Required fields
List of fields required to use this analytic.
- _time
- All_Traffic.dest_port
- All_Traffic.app
- All_Traffic.src
How To Implement
This search requires you to be ingesting your network traffic logs and populating the Network_Traffic
data model.
Known False Positives
A file server may experience high-demand loads that could cause this analytic to trigger.
Associated Analytic Story
RBA
Risk Score | Impact | Confidence | Message |
---|---|---|---|
25.0 | 50 | 50 | tbd |
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
Reference
Test Dataset
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 | version: 3