---
schema_version: "1"
id: be3330731cb8a6ae556f154ef9847dad7bed79f06a114f1e9dcdb806da08c1f8
title: Anomaly Detection in Log Center
collection: admin
version: current
locale: en-us
portal_url: https://docs.salesforce.com/admin/current/en-us/cc/264-0-0/b2c_anomaly_detection.md
canonical_url: https://help.salesforce.com/s/articleView?id=cc.b2c_anomaly_detection.htm&release=264.0.0&type=5
source_site: help.salesforce.com
content_type: documentation
content_hash: 52a05689e2b49cf4f6eb21fb0136adc633f1e399b8dc568d6feb9641a567ca22
description: Identify performance regressions, operational issues, and suspicious activity from unusual patterns in B2C Commerce Log Center. Log Center continuously analyzes incoming log data and compares the data with learned baseline behavior. When data deviates from the baseline and meets your thresholds, Log Center flags the anomaly. Each user can configure up to ten detectors. Use these guidelines to reduce false positives and improve detector accuracy.
products:
  - Commerce
  - B2C Commerce
release: "264"
---
# Anomaly Detection in Log Center

Identify performance regressions, operational issues, and suspicious activity from unusual patterns in B2C Commerce Log Center. Log Center continuously analyzes incoming log data and compares the data with learned baseline behavior. When data deviates from the baseline and meets your thresholds, Log Center flags the anomaly. Each user can configure up to ten detectors. Use these guidelines to reduce false positives and improve detector accuracy.

- Start with critical applications, such as authentication failures or error logs.
- Start with a higher threshold, then adjust severity and confidence over time.
- Review detector results regularly, and refine filters to show more relevant results.
- Create separate detectors for different environments to avoid mixing baselines.

## Reading the Anomaly Detector Results

To review a detector's findings, open its Anomaly Detector Results window. The window shows detected anomalies over time so that you can compare actual and predicted behavior and open the related logs for investigation.

Summary cards show the Total Number of Anomalies, the Anomaly Rate (%), and the Last Anomaly Occurrence for the selected period. Use the Time Range selector to set the period, from the last 15 minutes up to the last 14 days. You can also enter custom start and end times in UTC.

The chart plots two series over the selected period: the Actual log data trend and the Model predicted trend. Shaded plot bands mark the periods when the Log Center flagged an anomaly.

![Anomaly Detection Results in Log Center](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-cc-b2c_merchandiser_administrator-264-0-0-production-enus/ea76f372-6cfa-42a6-8e7c-05c6df7abed8/cc/b2c_merchandiser_administrator/images/b2c_log_center_anomaly_detection.png)

This table lists each flagged anomaly.

<table class="slds-table slds-table_bordered slds-max-medium-table_stacked slds-m-bottom_small ">
        <colgroup>
          <col style="width:50%">
          <col style="width:50%">
        </colgroup>
        <thead class="slds-text-title_caps ">
          <tr class="">
            <th scope="col" class="slds-cell-wrap" style="vertical-align:top;" id="d25013e81">Column</th>
            <th scope="col" class="slds-cell-wrap" style="vertical-align:top;" id="d25013e84">Description</th>
          </tr>
        </thead>
        <tbody>
          <tr class="">
            <td data-label="Column" class="slds-cell-wrap" style="vertical-align:top;" headers="d25013e81 ">Time</td>

            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d25013e84 ">
              <p style="margin-bottom:0;" dir="ltr">Time when the anomaly occurred, shown in UTC.</p>
            </td>

          </tr>
          <tr class="">
            <td data-label="Column" class="slds-cell-wrap" style="vertical-align:top;" headers="d25013e81 ">Severity</td>

            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d25013e84 ">
              <p style="margin-bottom:0;" dir="ltr">Severity score and category predicted by the model, from 0.0 to 1.0.
                Higher values indicate more severe anomalies.</p>
            </td>

          </tr>
          <tr class="">
            <td data-label="Column" class="slds-cell-wrap" style="vertical-align:top;" headers="d25013e81 ">Confidence</td>

            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d25013e84 ">
              <p style="margin-bottom:0;" dir="ltr">Estimated probability that the flagged anomaly is genuine, from 0.0 to
                1.0. Higher values indicate greater confidence.</p>
            </td>

          </tr>
          <tr class="">
            <td data-label="Column" class="slds-cell-wrap" style="vertical-align:top;" headers="d25013e81 ">Actual</td>

            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d25013e84 ">
              <p style="margin-bottom:0;" dir="ltr">Observed value for the metric or trend at the time of the anomaly.</p>
            </td>

          </tr>
          <tr class="">
            <td data-label="Column" class="slds-cell-wrap" style="vertical-align:top;" headers="d25013e81 ">Expected</td>

            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d25013e84 ">
              <p style="margin-bottom:0;" dir="ltr">Predicted value for the metric or trend at the time of the anomaly.</p>
            </td>

          </tr>
          <tr class="">
            <td data-label="Column" class="slds-cell-wrap" style="vertical-align:top;" headers="d25013e81 ">Deviation</td>

            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d25013e84 ">
              <p style="margin-bottom:0;" dir="ltr">Difference between the actual and expected values.</p>
            </td>

          </tr>
          <tr class="">
            <td data-label="Column" class="slds-cell-wrap" style="vertical-align:top;" headers="d25013e81 ">View Logs</td>

            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d25013e84 ">
              <p style="margin-bottom:0;" dir="ltr">Opens a log search filtered to about 30 minutes before and after the
                anomaly, so that you can investigate the underlying log entries.</p>
            </td>

          </tr>
        </tbody>
      </table>

Severity ranges:

  
| Severity | Score range | Chart and table color |
| --- | --- | --- |
| Low | Less than 0.3 | Green |
| Medium | 0.3 to less than 0.6 | Blue |
| High | 0.6 to less than 0.8 | Orange |
| Critical | 0.8 and higher | Red |

If you need help with validating a detector strategy, tuning thresholds, or troubleshooting results, contact your Salesforce admin or open a support case.

- **[Set Up Anomaly Detectors in Log Center](https://help.salesforce.com/s/articleView?id=cc.b2c_set_up_anomaly_detectors.htm&release=264.0.0&type=5)**  
    Identify unusual log patterns in B2C Commerce Log Center and optionally send alerts when anomalies meet your alert threshold. Log Center continuously analyzes incoming log data and compares the data with learned baseline behavior. When data deviates from the baseline and meets your thresholds, Log Center flags the anomaly.
- **[Manage Anomaly Detectors in Log Center](https://help.salesforce.com/s/articleView?id=cc.b2c_manage_anomaly_detectors.htm&release=264.0.0&type=5)**  
    View, edit, pause, resume, and delete anomaly detectors in B2C Commerce Log Center, and create a detector from a log search you already ran. What you can change depends on your Log Center role and the realms you can access.

#### See Also

- [Set Up Anomaly Detectors in Log Center](https://help.salesforce.com/s/articleView?id=cc.b2c_set_up_anomaly_detectors.htm&release=264.0.0&type=5 "Identify unusual log patterns in B2C Commerce Log Center and optionally send alerts when anomalies meet your alert threshold. Log Center continuously analyzes incoming log data and compares the data with learned baseline behavior. When data deviates from the baseline and meets your thresholds, Log Center flags the anomaly.")
- [Manage Anomaly Detectors in Log Center](https://help.salesforce.com/s/articleView?id=cc.b2c_manage_anomaly_detectors.htm&release=264.0.0&type=5 "View, edit, pause, resume, and delete anomaly detectors in B2C Commerce Log Center, and create a detector from a log search you already ran. What you can change depends on your Log Center role and the realms you can access.")