Count functions

Count functions detect anomalies when the number of events in a bucket is anomalous.

Use non_zero_count functions if your data is sparse and you want to ignore cases where the bucket count is zero.

Use distinct_count functions to determine when the number of distinct values in one field is unusual, as opposed to the total count.

Use high-sided functions if you want to monitor unusually high event rates. Use low-sided functions if you want to look at drops in event rate.

The machine learning features include the following count functions:

• count, high_count, low_count
• non_zero_count, high_non_zero_count, low_non_zero_count
• distinct_count, high_distinct_count, low_distinct_count

The count function detects anomalies when the number of events in a bucket is anomalous.

The high_count function detects anomalies when the count of events in a bucket are unusually high.

The low_count function detects anomalies when the count of events in a bucket are unusually low.

These functions support the following properties:

• by_field_name (optional)
• over_field_name (optional)
• partition_field_name (optional)

For more information about those properties, see the create anomaly detection jobs API.

 PUT _ml/anomaly_detectors/example1 {
  "analysis_config": {
    "detectors": [{
      "function" : "count"
    }]
  },
  "data_description": {
    "time_field":"timestamp",
    "time_format": "epoch_ms"
  }
}

This example is probably the simplest possible analysis. It identifies time buckets during which the overall count of events is higher or lower than usual.

When you use this function in a detector in your anomaly detection job, it models the event rate and detects when the event rate is unusual compared to its past behavior.

 PUT _ml/anomaly_detectors/example2 {
  "analysis_config": {
    "detectors": [{
      "function" : "high_count",
      "by_field_name" : "error_code",
      "over_field_name": "user"
    }]
  },
  "data_description": {
    "time_field":"timestamp",
    "time_format": "epoch_ms"
  }
}

If you use this high_count function in a detector in your anomaly detection job, it models the event rate for each error code. It detects users that generate an unusually high count of error codes compared to other users.

 PUT _ml/anomaly_detectors/example3 {
  "analysis_config": {
    "detectors": [{
      "function" : "low_count",
      "by_field_name" : "status_code"
    }]
  },
  "data_description": {
    "time_field":"timestamp",
    "time_format": "epoch_ms"
  }
}

In this example, the function detects when the count of events for a status code is lower than usual.

When you use this function in a detector in your anomaly detection job, it models the event rate for each status code and detects when a status code has an unusually low count compared to its past behavior.

 PUT _ml/anomaly_detectors/example4 {
  "analysis_config": {
    "summary_count_field_name" : "events_per_min",
    "detectors": [{
      "function" : "count"
    }]
  },
  "data_description": {
    "time_field":"timestamp",
    "time_format": "epoch_ms"
  }
}

If you are analyzing an aggregated events_per_min field, do not use a sum function (for example, sum(events_per_min)). Instead, use the count function and the summary_count_field_name property. For more information, see Aggregating data for faster performance.

The non_zero_count function detects anomalies when the number of events in a bucket is anomalous, but it ignores cases where the bucket count is zero. Use this function if you know your data is sparse or has gaps and the gaps are not important.

The high_non_zero_count function detects anomalies when the number of events in a bucket is unusually high and it ignores cases where the bucket count is zero.

The low_non_zero_count function detects anomalies when the number of events in a bucket is unusually low and it ignores cases where the bucket count is zero.

These functions support the following properties:

• by_field_name (optional)
• partition_field_name (optional)

For more information about those properties, see the create anomaly detection jobs API.

For example, if you have the following number of events per bucket:

The non_zero_count function models only the following data:

 PUT _ml/anomaly_detectors/example5 {
  "analysis_config": {
    "detectors": [{
      "function" : "high_non_zero_count",
      "by_field_name" : "signaturename"
    }]
  },
  "data_description": {
    "time_field":"timestamp",
    "time_format": "epoch_ms"
  }
}

If you use this high_non_zero_count function in a detector in your anomaly detection job, it models the count of events for the signaturename field. It ignores any buckets where the count is zero and detects when a signaturename value has an unusually high count of events compared to its past behavior.

The distinct_count function detects anomalies where the number of distinct values in one field is unusual.

The high_distinct_count function detects unusually high numbers of distinct values in one field.

The low_distinct_count function detects unusually low numbers of distinct values in one field.

These functions support the following properties:

• field_name (required)
• by_field_name (optional)
• over_field_name (optional)
• partition_field_name (optional)

For more information about those properties, see the create anomaly detection jobs API.

 PUT _ml/anomaly_detectors/example6 {
  "analysis_config": {
    "detectors": [{
      "function" : "distinct_count",
      "field_name" : "user"
    }]
  },
  "data_description": {
    "time_field":"timestamp",
    "time_format": "epoch_ms"
  }
}

This distinct_count function detects when a system has an unusual number of logged in users. When you use this function in a detector in your anomaly detection job, it models the distinct count of users. It also detects when the distinct number of users is unusual compared to the past.

 PUT _ml/anomaly_detectors/example7 {
  "analysis_config": {
    "detectors": [{
      "function" : "high_distinct_count",
      "field_name" : "dst_port",
      "over_field_name": "src_ip"
    }]
  },
  "data_description": {
    "time_field":"timestamp",
    "time_format": "epoch_ms"
  }
}

This example detects instances of port scanning. When you use this function in a detector in your anomaly detection job, it models the distinct count of ports. It also detects the src_ip values that connect to an unusually high number of different dst_ports values compared to other src_ip values.