﻿---
title: Information content functions
description: The information content functions detect anomalies in the amount of information that is contained in strings within a bucket. These functions can be used...
url: https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/5457/reference/machine-learning/ml-info-functions
products:
  - Elasticsearch
  - Machine Learning
---

# Information content functions
The information content functions detect anomalies in the amount of information that is contained in strings within a bucket. These functions can be used as a more sophisticated method to identify incidences of data exfiltration or C2C activity, when analyzing the size in bytes of the data might not be sufficient.
The machine learning features include the following information content functions:
- `info_content`, `high_info_content`, `low_info_content`


## Info_content, High_info_content, Low_info_content

The `info_content` function detects anomalies in the amount of information that is contained in strings in a bucket.
If you want to monitor for unusually high amounts of information, use `high_info_content`. If want to look at drops in information content, use `low_info_content`.
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](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ml-put-job).
```js
{
  "function" : "info_content",
  "field_name" : "subdomain",
  "over_field_name" : "highest_registered_domain"
}
```

If you use this `info_content` function in a detector in your anomaly detection job, it models information that is present in the `subdomain` string. It detects anomalies where the information content is unusual compared to the other `highest_registered_domain` values. An anomaly could indicate an abuse of the DNS protocol, such as malicious command and control activity.
<note>
  In this example, both high and low values are considered anomalous. In many use cases, the `high_info_content` function is often a more appropriate choice.
</note>

```js
{
  "function" : "high_info_content",
  "field_name" : "query",
  "over_field_name" : "src_ip"
}
```

If you use this `high_info_content` function in a detector in your anomaly detection job, it models information content that is held in the DNS query string. It detects `src_ip` values where the information content is unusually high compared to other `src_ip` values. This example is similar to the example for the `info_content` function, but it reports anomalies only where the amount of information content is higher than expected.
```js
{
  "function" : "low_info_content",
  "field_name" : "message",
  "by_field_name" : "logfilename"
}
```

If you use this `low_info_content` function in a detector in your anomaly detection job, it models information content that is present in the message string for each `logfilename`. It detects anomalies where the information content is low compared to its past behavior. For example, this function detects unusually low amounts of information in a collection of rolling log files. Low information might indicate that a process has entered an infinite loop or that logging features have been disabled.