Detecting anomalies from logs

The following video (2:10) provides a high-level summary of the anomaly detection feature in BMC Helix Log Analytics .

https://youtu.be/jG-Owi1tusc

Process overview

The following procedure is used by BMC Helix Log Analytics to detect anomalous logs:

1. Processing the ingested logs.
   An administrator configures alert policies to identify anomalies in the logs. These logs are processed to remove dates, special characters, and unnecessary keywords. The cleaning process uses regular expressions to remove dates and filter characters.

1. Detecting anomalies.
    BMC Helix Log Analytics can detect anomalies in the following ways:
   • Abnormal log records
   • Based on the log volume
2. Analyzing anomalous logs.
   Use the Explorer tab to analyze log anomalies.
   Because of the anomaly detection alert policies, log anomaly events are generated in BMC Helix Operations Management. Use the Events page in BMC Helix Operations Management to analyze the log anomaly events.
   For more information, see Analyzing anomalous logs and anomaly events.

Anomaly detection based on abnormal log patterns

BMC Helix Log Analytics uses hierarchical clustering and cross-similarity techniques to detect anomalies in logs. Anomalies are detected by using the following method:

1. The hierarchical clustering identifies anomalies from the first batch of logs.
    BMC Helix Log Analytics starts detecting anomalies after a default initial training period of 15 minutes. The training period is used to determine false positives.

   Important

   Logs are ingested as a single file for any policy. If multiple log files match the selection criteria defined in the policy, the logs from all files are combined in a single file.

2. The identified anomalies are stored in a tags table in the binary format for further analysis.
3. For every subsequent batch, the system performs a cross-similarity check between newly identified anomalies and the data stored in the tags binary.
   The default anomaly tag limit is 2000. That means when the tag count reaches the maximum limit, 20% of the oldest tags are removed.
   The recommended range for the anomaly tag limit is between 1000 and 2000. If the limit exceeds this range, it can lead to issues in generating log anomalies.
   The anomalies use the context extraction technique for quick insights, where the top three keywords are extracted from the anomalies.
4. Any newly identified anomalies that differ from the previous data are considered new anomalies.
    The default aging period of an anomaly is two hours. That means if an anomaly is detected and reappears within two hours, the behavior is not considered anomalous. However, if an anomaly is detected and reappears after two hours, the behavior is flagged as anomalous.
   The complete list of anomalies includes both new and existing anomalies in tags
   
   

Anamolous log record examples

This section provides examples of anomalous logs.

Configuring anomaly detection

As an administrator, create alert policies in BMC Helix Log Analytics to receive anomaly notifications. After you enable an alert policy, you can use the  Explorer  tab to analyze anomalous logs.

To create an alert policy for anomaly detection

1. BMC Helix Log Analytics, navigate to the Alerts > Alert Policies button page and click Create.
2. Add the policy information by performing the following steps:
   1. Enter a unique name and description for the alert.
   2. In the  Precedence  field, set a precedence for the policy.
      The precedence number defines the priority for executing the policy. A policy with a lower precedence number is executed first. 
3. In the  Policy Selection Criteria  section, perform the following steps:

   1. Configure the condition for which the event will be generated.
      For example, enter status Equals 401 AND filename EQUALS BMC_Apache_SantaClara.log. When you click in the box , you are prompted to make a selection. Each time you make a selection, you are progressively prompted to make another selection. 
      The selection criteria consist of an opening parenthesis, followed by the slot name, the operator, the slot value (which can be a string based on the type of slot selected), and the closing parenthesis. You can optionally select the logical operator AND or OR to add additional conditions. Specifying the opening and closing parentheses is optional.

      Important

      • The values that you enter for a field in the selection criteria are case-sensitive. For example, if the host name is WebServer.example.com, add the selection criteria as (host_name Equals WebServer.example.com). If you enter (host_name Equals webserver.example.com), the event is not generated.
      • Ideally,  each policy should be for a single data source.
   2. To group occurrences of a condition, perform one of the following actions:
      • In the  Group by  field, enter the values by which you want to group occurrences of a condition.
        For example, to group all occurrences of status 401 on a particular host name, enter the host name. You can enter a maximum of three values, but one must be the host name.
      • Click in the  Group by  field and select an appropriate option.
        The default value is  log_source_host .
   3. Select the Anomaly Detection button and perform the following steps:
      1. From the Log Attribute list, select the field that contains the log message.
      2. Select the type of event that you want to create. 
         If it is not Message or Log, select Custom and in the Log Attribute Value field, enter the field that contains the log message.
   4. In the Alert Parameters section, complete the following steps:
      • To add host name to the event, in the  Alert Parameters  section, perform one of the following actions:
        This value helps you correlate events in BMC Helix AIOps.
        • In the  Hostname  field, enter a host name.
        • Click in the  Hostname  field and select the appropriate option.
          The default value is  log_source_host .
      • In the Message field, change the default message, if required.
        To use a log field value in the message, put double curly brackets around the field name such as {{ $.location }} .
      • In  Additional Details, configure additional event parameters such as source identifier.
        These values are set for the generated event.

4. For data-level access control, select one or more user groups from the User Group list.

   Important

   Make sure that you select the same user group that you selected in the collection policy.

   With this setting, the system generates alerts, and only the selected user group can access them.
   Alerts are not generated if you select different user groups in the collection and alert policies.

5. Enable and save the policy by performing the following steps:
   1. Select Enable Policy.
      You can choose to enable the collection policy later.
   2. Click Save.
      View all your policies on the Alert Policies page.

For instructions about editing an alert policy, see WIP-Managing-alert-policies.

Configuring anomaly detection settings

BMC Helix Log Analytics provides anomaly detection settings to help you accurately identify unusual patterns and potential issues in your log data. Use these settings to configure the following parameters that determine how anomalies are detected.

With these settings, you can configure the parameters that are described in the following table:

To configure anomaly detection settings, perform the following steps as an administrator:

1. From the Configurations menu, select Anomaly Detection Settings.
2. On the Anomaly Detection Settings page, modify Training Period, Aging Period, and Anomaly Tag Limit according to your requirements.
3. Click Save.
   To set these values to the default, click Reset to Default.
   
   
   

Analyzing anomalous logs and anomaly events

Use BMC Helix Log Analytics to analyze  anomalous logs. Use BMC Helix Operations Management to analyze the related anomaly events.

For more information, see WIP-page-restructuring-Analyzing-anomalous-logs-and-anomaly-events.

Visualizing log anomaly events in BMC Helix Dashboards

Use the Self Monitoring dashboard in BMC Helix Dashboards to visualize anomalous log data.

The following image displays the Self Monitoring dashboard:

Use the Search parameter column to open the Explorer tab in BMC Helix Log Analytics.

Use the Event Details column to open the Event Details page in BMC Helix Operations Management.

For more information about the Self Monitoring dashboard, see  Self-monitoring dashboard in the BMC Helix Dashboards documentation .

Where to go from here

For information about creating a static alert policy, see WIP-Managing-alert-policies.

For information about log events, see WIP-Log-events.