Monitoring Linux resource usage with Wazuh

May 4th 2023 | by Iseoluwa Titiloye Oyeniyi | Wazuh 4.4

Monitoring Linux resources is crucial to optimizing performance and also to maintain the security of an organization’s data and network. Maintaining the health of a computer system is important for the proper provisioning of its services, and this can be ensured by having a comprehensive view of the state of its resources. Organizations are able to stay ahead of potential performance issues by knowing how endpoint resources are utilized. Wazuh is an open source solution that provides the means to monitor and manage the security and performance of computer systems.

In this blog post, we describe how to use Wazuh in monitoring Linux system resource usage and in turn, maintain security.

Linux performance metrics

Metric data can be generated on every endpoint with the use of different utilities. This data provides insight into how endpoint resources are used. Wazuh can collect, index, analyze, and visualize endpoint metric data.

There are primary metrics to consider when monitoring Linux endpoints. The four primary metrics you should track are CPU usage, CPU load, memory utilization, and disk usage.

1. CPU usage: This is the percentage of time that a CPU spends working on tasks that are non-idle.

Linux splits each CPU time into slices and uses the CPU scheduler to allocate these time slices to each task. You can obtain the CPU usage percentage of a Linux endpoint with utilities such as top. A sample result is shown below:

$ top -bn1 | grep Cpu

%Cpu(s):  6.6 us,  2.0 sy,  0.0 ni, 91.3 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st

Where:

us (user CPU time) is the percentage of time spent running user processes.
sy (system CPU time) is the percentage of time spent running the kernel.
ni (user nice CPU time) is the percentage of time spent running niced user processes i.e. user space processes that are assigned with execution priority.
id (idle CPU time) is the percentage of time spent in the kernel idle handler. The CPU usage is considered to be idle when there are no ongoing I/O operations and the runnable queue is empty.
wa (i/o wait CPU time) is the percentage of time that the CPU waits for the completion of disk input/output operations.
hi (hardware interrupt request) is the percentage of time that the CPU spends handling/servicing hardware interrupts.
si (software interrupt request) is the percentage of time that the CPU spends handling/servicing software interrupts.
st (steal time) is the percentage of time that the virtual machine process is waiting on the physical CPU for its CPU time.

Algorithmically, we can calculate the CPU usage percentage in Linux as shown below:

Idle = idle + iowait

NonIdle = user + system + nice + hardirq + softirq + steal


Total = Idle + NonIdle

Hence:

CPU Utilization (%) = ( NonIdle / Total ) * 100

2. CPU load: This measures the number of programs that are awaiting or utilizing a processor core at a time. CPU load is different from CPU usage as it is a measure of the average number of programs that are either making use of CPU processing time or waiting for CPU processing time.

You can get the CPU load average with the top or uptime utility. A sample result is shown below:

$ uptime

 10:31:03 up  8:42,  2 users,  load average: 2.53, 2.61, 2.84

Where:

The CPU load in the last one (1) minute is 2.53.
The CPU load in the last five (5) minutes is 2.61.
The CPU load in the last fifteen (15) minutes is 2.84.

3. Memory utilization: This refers to the percentage of a computer’s available memory that is currently being used by the system or running programs. Users can identify underused and overused servers by monitoring system memory. This creates an opportunity to effectively redistribute loads and optimize memory usage.

You can obtain memory usage information with the use of utilities such as free. A sample result providing insight into the amount of used and unused memory is shown below:

$ free

          total     used      free     shared     buff/cache     available
Mem:      15Gi      7.1Gi     6.1Gi    940Mi      2.2Gi          7.0Gi
Swap:     2.0Gi     945Mi     1.1Gi

We show how to calculate the percentage of memory utilization below:

Memory Utilization (%) = 100 - ((( MemFree + Buffers + Cached) * 100 ) / totalMemory )

4. Disk usage: This is the percentage of disk space in use. This is an important metric that creates an opportunity for users to prevent potential data loss, endpoint lag, and disk failure.

You can get the disk usage of the file system mounted at the root (/) directory with the df utility. A sample output result is shown below:

$ df -h /

Filesystem  	 Size           Used         Avail      Use%   Mounted on
/dev/nvme0n1p2   528256304      389655668    111693196  78%    /

We show how to calculate the percentage of disk utilization below:

Disk Utilization(%) = ( DiskUsed / DiskTotal ) * 100

Overall, monitoring endpoint resources such as CPU, memory and disk usage can provide vital insights into the health of individual devices on a network. This helps in detecting abnormal behavior that may be indicative of security threats and allows you to prevent data breaches.

Monitoring Linux with Wazuh

To demonstrate Wazuh capabilities for monitoring Linux resources, we set up the following infrastructure:

1. A pre-built ready-to-use Wazuh OVA 4.4.1. Follow this guide to download the virtual machine. This endpoint hosts the Wazuh central components (Wazuh server, Wazuh indexer, and Wazuh dashboard).

2. Ubuntu 22.04 endpoint with Wazuh agent installed and enrolled to the Wazuh server. A Wazuh agent can be installed by following the deploying Wazuh agents on Linux endpoints guide.

Configuration

We will make use of the Wazuh command monitoring capability to query and monitor the endpoint’s performance metrics.

The Wazuh command monitoring module allows you to remotely execute specified commands on monitored endpoints, providing a way to gather important information or perform tasks. Once executed, the output generated by these commands is captured as log data, which can be analyzed to identify potential security threats or gain valuable insights into the behavior of your network.

In this blog post, we configure the command monitoring module to periodically execute commands to query system resources.

Ubuntu

Take the following steps to configure the Wazuh command monitoring module.

1. Edit the Wazuh agent /var/ossec/etc/ossec.conf file and add the following command monitoring configuration within the <ossec_config> block:

<!-- CPU, memory, disk metric -->
  <localfile>
     <log_format>full_command</log_format>
     <command>echo $(top -bn1 | grep Cpu | awk '{print $2+$4+$6+$12+$14+$16}' ; free -m | awk 'NR==2{printf "%.2f\t\t\n", $3*100/$2 }' ; df -h | awk '$NF=="/"{print $5}'|sed 's/%//g')</command>
     <alias>general_health_metrics</alias>
     <out_format>$(timestamp) $(hostname) general_health_check: $(log)</out_format>
     <frequency>30</frequency>
  </localfile>

<!-- load average metrics -->
  <localfile>
     <log_format>full_command</log_format>
     <command>uptime | grep load | awk '{print $(NF-2),$(NF-1),$NF}' | sed 's/\,\([0-9]\{1,2\}\)/.\1/g'</command>
     <alias>load_average_metrics</alias>
     <out_format>$(timestamp) $(hostname) load_average_check: $(log)</out_format>
     <frequency>30</frequency>
  </localfile>

<!-- memory metrics -->
  <localfile>
     <log_format>full_command</log_format>
     <command>free --bytes| awk 'NR==2{print $3,$7}'</command>
     <alias>memory_metrics</alias>
     <out_format>$(timestamp) $(hostname) memory_check: $(log)</out_format>
     <frequency>30</frequency>
  </localfile>

<!-- disk metrics -->
  <localfile>
     <log_format>full_command</log_format>
     <command>df -B1 | awk '$NF=="/"{print $3,$4}'</command>
     <alias>disk_metrics</alias>
     <out_format>$(timestamp) $(hostname) disk_check: $(log)</out_format>
     <frequency>30</frequency>
  </localfile>

Note

You can use the centralized configuration to distribute this setting across multiple monitored endpoints. However, remote commands are disabled by default for security reasons and have to be explicitly enabled on each agent.

2. Restart the Wazuh agent to apply this change:

$ sudo systemctl restart wazuh-agent

Wazuh server

1. Add the following decoders to /var/ossec/etc/decoders/local_decoder.xml to decode the logs from the command monitoring module:

<!-- CPU, memory, disk metric -->
<decoder name="general_health_check">
     <program_name>general_health_check</program_name>
</decoder>

<decoder name="general_health_check1">
  <parent>general_health_check</parent>
  <prematch>ossec: output: 'general_health_metrics':\.</prematch>
  <regex offset="after_prematch">(\S+) (\S+) (\S+)</regex>
  <order>cpu_usage_%, memory_usage_%, disk_usage_%</order>
</decoder>

<!-- load average metric -->
<decoder name="load_average_check">
     <program_name>load_average_check</program_name>
</decoder>

<decoder name="load_average_check1">
  <parent>load_average_check</parent>
  <prematch>ossec: output: 'load_average_metrics':\.</prematch>
  <regex offset="after_prematch">(\S+), (\S+), (\S+)</regex>
  <order>1min_loadAverage, 5mins_loadAverage, 15mins_loadAverage</order>
</decoder>

<!-- Memory metric -->
<decoder name="memory_check">
     <program_name>memory_check</program_name>
</decoder>

<decoder name="memory_check1">
  <parent>memory_check</parent>
  <prematch>ossec: output: 'memory_metrics':\.</prematch>
  <regex offset="after_prematch">(\S+) (\S+)</regex>
  <order>memory_used_bytes, memory_available_bytes</order>
</decoder>

<!-- Disk metric -->
<decoder name="disk_check">
     <program_name>disk_check</program_name>
</decoder>

<decoder name="disk_check1">
  <parent>disk_check</parent>
  <prematch>ossec: output: 'disk_metrics':\.</prematch>
  <regex offset="after_prematch">(\S+) (\S+)</regex>
  <order>disk_used_bytes, disk_free_bytes</order>
</decoder>

2. Create rules to detect metrics in the logs from the command monitoring module. Add the rules to the custom rules file /var/ossec/etc/rules/local_rules.xml on the Wazuh server:

<group name="performance_metric,">

<!-- CPU, Memory and Disk usage -->
<rule id="100054" level="3">
  <decoded_as>general_health_check</decoded_as>
  <description>CPU | MEMORY | DISK usage metrics</description>
</rule>

<!-- High memory usage -->
<rule id="100055" level="12">
  <if_sid>100054</if_sid>
  <field name="memory_usage_%">^[8-9]\d|100</field>
  <description>Memory usage is high: $(memory_usage_%)%</description>
  <options>no_full_log</options>
</rule>

<!-- High CPU usage -->
<rule id="100056" level="12">
  <if_sid>100054</if_sid>
  <field name="cpu_usage_%">^[8-9]\d|100</field>
  <description>CPU usage is high: $(cpu_usage_%)%</description>
  <options>no_full_log</options>
</rule>

<!-- High disk usage -->
<rule id="100057" level="12">
  <if_sid>100054</if_sid>
  <field name="disk_usage_%">^[7-9]\d|100</field>
  <description>Disk space is running low: $(disk_usage_%)%</description>
  <options>no_full_log</options>
</rule>

<!-- Load average check -->
<rule id="100058" level="3">
  <decoded_as>load_average_check</decoded_as>
  <description>load average metrics</description>
</rule>

<!-- memory check -->
<rule id="100059" level="3">
  <decoded_as>memory_check</decoded_as>
  <description>Memory metrics</description>
</rule>

<!-- Disk check -->
<rule id="100060" level="3">
  <decoded_as>disk_check</decoded_as>
  <description>Disk metrics</description>
</rule>

</group>

Where:

Rule ID 100054 is the base rule for detecting resource monitoring events.
Rule ID 100055 is triggered when the memory utilized exceeds 80%.

Rule ID 100056 is triggered when the CPU utilized exceeds 80%.
Rule ID 100057 is triggered when the disk space used exceeds 70%.
Rule ID 100058 is triggered when a CPU load average check is done.
Rule ID 100059 is triggered when a memory metric check is done.
Rule ID 100060 is triggered when a disk metrics check is done.

3. Restart the Wazuh manager to apply these changes:

$ sudo systemctl restart wazuh-manager

You will see relevant alerts on the Wazuh dashboard after restarting the Wazuh manager service.

Wazuh dashboard

The newly added custom fields, data.1min_loadAverage, data.5min_loadAverage, data.15mins_loadAverage, data.cpu_usage_%, data.memory_usage_%, data.disk_usage_%, data.disk_free_bytes, data.disk_used_bytes, memory_used_bytes, data.memory_available_bytes will be displayed as an unknown field as highlighted in the figure below. This is because the Wazuh dashboard may not recognize the new fields. You need to update the index pattern in Kibana to include the new fields.

1. Refresh the index pattern by selecting the menu icon in the top left corner and navigate to Management -> Stack Management -> Index Patterns -> wazuh-alerts-*. Click the refresh button on that index pattern page as shown below.

2. Verify the custom fields now exist in the default string format.

You can also verify this by navigating to Security events and confirming that the custom field is no longer identified as an unknown field but rather a String field.

Alert visualization

Visualize the triggered alerts by following the steps below:

Load average: Click on the menu item in the top left corner, navigate to the Discover section, and apply the rule.id:100058 filter in the search bar to query the alerts. Also, add the following fields as columns by hovering on each field and clicking the + icon beside it, agent.name, rule.description, data.1min_loadAverage, data.5mins_loadAverage, data.15mins_loadAverage. Save the searches as Load average metrics by clicking on the Save button on the top right.

CPU, Memory, and Disk metric usage: Click on the menu item in the top left corner, navigate to the Discover section, and apply the rule.id:100054 filter in the search bar to query the alerts. Also, add the following fields as columns by hovering on each field and clicking the + icon beside it, agent.name, rule.description, data.cpu_usage_%, data.memory_usage_%, data.disk_usage_%. Save the searches as General health metrics by clicking on the Save button on the top right.

Disk usage: Click on the menu item in the top left corner, navigate to the Discover section, and apply the rule.id:100060 filter in the search bar to query the alerts. Also, add the following fields as columns by hovering on each field and clicking the + icon beside it, agent.name, rule.description, data.disk_free_bytes, data.disk_used_bytes. Save the searches as Disk metrics by clicking on the Save button on the top right.

Memory usage: Click on the menu item in the top left corner, navigate to the Discover section, and apply the rule.id:100059 filter in the search bar to query the alerts. Also, add the following fields as columns by hovering on each field and clicking the + icon beside it, agent.name, rule.description, data.memory_used_bytes, data.memory_available_bytes. Save the searches as Memory metrics by clicking on the Save button on the top right.

Building custom visualizations and dashboards

We can further improve the viewing of existing metrics with the use of custom dashboards, visualizations, and graphs. This presents a dynamic representation for monitoring system performance.

Modifying the Wazuh template

To use the alerts to create visualizations and dashboards, we need to set the data type of all custom fields to double. By default, the Wazuh indexer analyzes values from existing alerts as string data types. To change the default data type from string to double, do the following:

Wazuh server

1. Add the custom fields in the Wazuh template. Find the data section in the /etc/filebeat/wazuh-template.json file, and add the highlighted custom fields to the data properties section:

{
 "order": 0,
 "index_patterns": [
    "wazuh-alerts-4.x-*",
    "wazuh-archives-4.x-*"
  ],
 "settings": {
   ...
  },
 "mappings": {
    "dynamic_templates": [
  	{
   ...
 "data": {
   "properties": {
         "1min_loadAverage": {
                 "type": "double"
               },
         "5mins_loadAverage": { 
                 "type": "double"
               },
         "15mins_loadAverage": {
                 "type": "double"
               },
         "cpu_usage_%": { 
                 "type": "double"
               },
         "memory_usage_%": {
                 "type": "double"
               },
         "memory_available_bytes": { 
                 "type": "double"
               },
         "memory_used_bytes": {
                  "type":  "double"
               },
         "disk_used_bytes": {
                 "type": "double"
               },
         "disk_free_bytes": { 
                 "type": "double"
               },
         "disk_usage_%": {
                 "type": "double"
               },
     "audit": {
       "properties": {
                 "acct": {
                     "type": "keyword"

2. To apply the changes to the Wazuh template, run the command below:

$ sudo filebeat setup -index-management

An expected output is shown below:

ILM policy and write alias loading not enabled. 

Index setup finished.

Note

Modifying existing fields is not permitted in the Wazuh indexer. Once an index is created with some data fields, any changes to the existing fields on the live index are not allowed. Regardless, there is a walkaround; re-indexing.

Re-indexing data

Re-indexing data is the process of moving data from one index to another while possibly transforming the data in the process. This is done when there are changes to the data structure or mappings that require re-organizing or updating the index.

Wazuh dashboard

1. Re-index your data. To use this technique, we will copy the data from the original index to a new index with updated schema definitions. Click on the upper-left menu icon and navigate to Management -> Dev Tools to access the console. We use this console to run the necessary queries to re-index the existing data.

2. Check existing indices:

GET _cat/indices

The output below shows the names of the existing indices and corresponding creation dates:

green open wazuh-alerts-4.x-2023.04.24 TSj3p71XR5qTvwNR3JicwQ 3 0	3 0  53.5kb  53.5kb
green open wazuh-alerts-4.x-2023.04.23 VjfArkNxSOK8-DBht8YnSg 3 0  480 0   1.1mb   1.1mb
green open wazuh-alerts-4.x-2023.04.22 svIJ4pKvRk2fILuMNFCxtQ 1 0	0 0	208b	208b

3. Retrieve data about the index from the Wazuh indexer using a GET request. This confirms that the added custom fields are of the keyword type. In Wazuh, indices are created with the format wazuh-alerts-4.x-YYYY.MM.DD. Here, we will make use of the latest index wazuh-alerts-4.x-2023.04.24. Ensure you replace this index value with that of your latest index:

GET /wazuh-alerts-4.x-2023.04.24

An expected output is shown below:

…
        	},
        	"id": {
          	"type": "keyword"
        	},
        	"integration": {
          	"type": "keyword"
        	},
        	"memory_available_bytes": {
          	"type": "keyword"
        	},
        	"memory_usage_%": {
          	"type": "keyword"
        	},
        	"memory_used_bytes": {
          	"type": "keyword"
        	},
        	"netinfo": {
…

4. Extract the data from your latest source index to the new destination index named wazuh-alerts-4.x-backup with the re-index API. Replace the source index with your own latest index name value.

POST _reindex
{
  "source": {
	"index": "wazuh-alerts-4.x-2023.04.24"
  },
  "dest": {
	"index": "wazuh-alerts-4.x-backup"
  }
}

The new index with the new schema will be ready to use once the re-indexing is complete. Wazuh indexer returns a 200 – OK status code along with a JSON response that provides details about the operation. These details include the number of documents that were reindexed, the time it took to complete the operation, and any errors or warnings that occurred during the process.

5. Delete the old index. Replace the index name value with your own:

DELETE /wazuh-alerts-4.x-2023.04.24

6. Re-index the data from the backup index wazuh-alerts-4.x-backup to a new index with the original name wazuh-alerts-4.x-2023.04.24. This should be replaced with your own index name. This is done to apply our data type changes:

POST _reindex
{
  "source": {
	"index": "wazuh-alerts-4.x-backup"
  },
  "dest": {
	"index": "wazuh-alerts-4.x-2023.04.24"
  }
}

7. Delete the earlier created backup index:

DELETE /wazuh-alerts-4.x-backup

8. Verify the string data type for the custom fields has changed to the double data type in the updated index. Ensure you replace this index value with that of your own index::

GET /wazuh-alerts-4.x-2023.04.24

An expected output is shown below:

…
        	},
        	"integration": {
          	"type": "keyword"
        	},
        	"memory_available_bytes": {
          	"type": "double"
        	},
        	"memory_usage_%": {
          	"type": "double"
        	},
        	"memory_used_bytes": {
          	"type": "double"
        	},
        	"netinfo": {
…

9. Refresh the field list by navigating to Management -> Stack Management -> Index Patterns -> wazuh-alerts-* and clicking the refresh button on that index pattern page as shown below.

10. Verify the updated fields are now in the number format by navigating to the Security events.

Changing data type formats

Considering some of the data being monitored represents disk storage and memory usage, representing this numerical data in bytes format would make it easier to understand and compare the values. To change the data format for specific fields from the default number to bytes, do the following:

Wazuh dashboard

1. Navigate to Management -> Stack Management -> Index Patterns -> wazuh-alerts-*.

2. Search for the following custom fields individually; data.memory_available_bytes, data.memory_used_bytes, data.disk_free_bytes, data.disk_used_bytes, and click on the pencil icon highlighted to the right.

3. Change the default numerical format setting to the bytes format and save the field.

Creating custom visualizations

Wazuh dashboard

Click the upper-left menu icon and navigate to OpenSearch Dashboards -> Visualize -> Create new visualization.

We use the line visualization format and wazuh-alerts-* as the index pattern name. This will create a data tab where the required filters are specified. The image below shows a sample visualization using the settings above.

The Wazuh dashboard visualization builder contains two aggregation objects; metric and bucket aggregation as shown above:

The metric aggregation contains the actual values of the metric to be calculated. It is typically represented on the Y-axis.
The bucket aggregation determines how the data is segmented or grouped such as by date. It is typically represented on the X-axis.

Both the Y-axis and the X-axis are used to plot the data points on a visualization chart. Follow the instructions below to create individual visualizations for this blog post.

CPU usage visualization

a. From the Visualize tab, select the line visualization format and use wazuh-alerts-* as the index pattern name.

b. On the Y-axis, in Metrics, set the following values:

Aggregation = Max
Field = data.cpu_usage_%
Custom label = Cpu_Usage %

c. Add an X-axis in Buckets and set the following values:

Aggregation = Date Histogram
Field = timestamp
Minimum interval = Minute

d. Click the Update button.

e. Click the upper-right Save button and assign a title to save the visualization.

Load average visualization

a. From the Visualize tab, click Create visualization, select the line visualization format, and use wazuh-alerts-* as the index pattern name.

b. On the Y-axis of Metrics, set the following values:

Aggregation = Max
Field = data.1min_loadAverage
Custom label = 1 min load average

c. Add an additional Y-axis metric

Aggregation = Max
Field = data.5min_loadAverage
Custom label = 5 min load average

d. Add a final Y-axis metric

Aggregation = Max
Field = data.15min_loadAverage
Custom label = 15 min load average

e. Add an X-axis in Buckets and set the following values:

Aggregation = Date Histogram
Field = timestamp
Minimum interval = Minute

f. Click the Update button.

g. Click the upper-right Save button and assign a title to save the visualization.

Disk usage visualization

a. From the Visualize tab, click Create Visualization, select the line visualization format, and use wazuh-alerts-* as the index pattern name.

b. On the Y-axis of Metrics, set the following values:

Aggregation = Max
Field = data.disk_usage_%
Custom label = disk usage %

c. Add an X-axis in Buckets and set the following values:

Aggregation = Date Histogram
Field = timestamp
Minimum interval = Minute

d. Click the Update button.

e. Click the upper-right Save button and assign a title to save the visualization.

Memory usage visualization

a. From the Visualize tab, click Create Visualization, select the line visualization format, and use wazuh-alerts-* as the index pattern name.

b. On the Y-axis of Metrics data, set the following values:

Aggregation = Max
Field = data.memory_usage_%
Custom label = memory usage %

c. Add an X-axis in Bucket and set the following values:

Aggregation = Date Histogram
Field = timestamp
Minimum interval = Minute

d. Click the Update button.

e. Click the upper-right Save button and assign a title to save the visualization.

Memory size visualization

a. From the Visualize tab, click Create Visualization, select the area visualization format, and use wazuh-alerts-* as the index pattern name.

b. On the Y-axis of Metrics, set the following values:

Aggregation = Max
Field = data.memory_available_bytes
Custom label = memory available

c. Add an additional Y-axis metric

Aggregation = Max
Field = data.memory_used_bytes
Custom label = memory used

d. Add an X-axis in Bucket and set the following values:

Aggregation = Date Histogram
Field = timestamp
Minimum interval = Minute

e. Click the Update button.

f. Click the upper-right Save button and assign a title to save the visualization.

Disk table visualization

a. From the Visualize tab, click Create Visualization, select the data table visualization format and use wazuh-alerts-* as the index pattern name.

b. On Metric in Metrics, set the following values:

Aggregation = Max
Field = data.disk_free_bytes
Custom label = disk free

c. Add an additional metric

Aggregation = Max
Field = data.disk_used_bytes
Custom label = disk used

d. Click the Update button.

e. Click the upper-right Save button and assign a title to save the visualization.

Memory table visualization

a. From the Visualize tab, click Create Visualization, select the data table visualization format and use wazuh-alerts-* as the index pattern name.

b. On the Metric in Metrics data, set the following values:

Aggregation = Max
Field = data.memory_available_bytes
Custom label = memory available

c. Add an additional metric

Aggregation = Max
Field = data.memory_used_bytes
Custom label = memory used

d. Click the Update button.

e. Click the upper-right Save button and assign a title to save the visualization.

The GIF below shows sample visualizations that were created using the custom metrics:

Creating custom dashboard

Wazuh dashboard

1. Click on the upper-left menu icon and navigate to OpenSearch Dashboards -> Dashboard -> Create new dashboard.

2. Click Add an existing and select the newly created visualizations to populate the dashboard.

3. Save the dashboard by selecting the save option on the top-right navigation bar.

The GIF below shows a sample dashboard containing the newly created visualizations.

Generate a report

Wazuh dashboard

1. Click on the upper-left menu icon and navigate to OpenSearch Dashboards -> Dashboard page.

2. Select a previously created dashboard.

3. Click Reporting on the navigation bar at the top right corner.

4. Select either the PDF or PNG format and download the report.

Conclusion

The performance of an application is negatively affected when system resources are insufficient. It is crucial to monitor system resources in order to stay ahead of performance regressions and contribute to the overall security monitoring of endpoints. In this article, we have demonstrated how to monitor Linux resources using Wazuh.

References

Accurate calculation of CPU usage given in percentage in Linux – Stack Overflow

How is the total memory utilization calculated for a Linux server monitor

If you have any questions about this, join our Slack community channel!

Monitoring Linux resource usage with Wazuh

Linux performance metrics

Monitoring Linux with Wazuh

Configuration

Ubuntu

Wazuh server

Wazuh dashboard

Alert visualization

Building custom visualizations and dashboards

Modifying the Wazuh template

Wazuh server

Re-indexing data

Wazuh dashboard

Changing data type formats

Wazuh dashboard

Creating custom visualizations

Wazuh dashboard

CPU usage visualization

Load average visualization

Disk usage visualization

Memory usage visualization

Memory size visualization

Disk table visualization

Memory table visualization

Creating custom dashboard

Wazuh dashboard

Generate a report

Wazuh dashboard

Conclusion

References

Wazuh and Oznet sign a partnership agreement

Integrating Mimecast with Wazuh

Datasec upgrades to Wazuh Platinum partnership

Filtering security data with the Wazuh Query Language