Frequency

Calculates the characteristic frequency of an object's actions (repetition of actions over a period of time). Used to detect anomalies, such as:

• a user launched a rarely used process
• a file with a rare file extension for the user was used

Algorithm Description

1. General and temporal filters are applied to the source index data
2. Each data record is brought to a common form according to the settings of the processed fields
3. Data is divided by unique combinations of values of the processed fields
4. Each part of the data obtained in step 3 is divided into intervals, and the number of documents in each interval is counted
5. Statistics are calculated based on the number of documents for each field division

Input Parameters

• Filter - general filter for sources (using expressions from the search command)
• Index for results - index where the execution results are recorded
• Fields to be processed - mapping of source fields to result fields
  • Name - the name of the field in the results index
  • Index template / Field name in the source - a list of index templates and corresponding fields in them that will be extracted into the result
• Interval - the size of the time intervals into which the source data is divided. Examples of filling: 1y year, 1M month, 1d day, 1H hour, 1m minute, 1s second
• Pass intervals without data - empty intervals are not considered in the statistics calculation
• Exception Settings — allows you to define rules for excluding data from calculations. Clicking this option opens a modal window where you can add conditions to exclude specific objects

Input Data

Input data is determined by the indices and time interval in the base settings.

Output Data

As a result of the algorithm execution, several records appear in the results index. Each record contains statistics for all time intervals for the processed fields.

• _meta.calculation.id - the identifier of the algorithm setting in the profiling policy
• _meta.calculation.type - the type of algorithm
• _meta.execution.start_time - the time the profiling policy was launched
• _meta.execution.id - the identifier of the profiling policy launch
• _meta.object.identity - an array of UBA object identifiers
• _meta.object.id - the technical identifier of the UBA object
• _calculation - the result of the algorithm execution
• _calculation.extended_stats - extended statistics for all intervals
• _calculation.percentiles - percentiles for all intervals
• _calculation.span - the size of the interval
• _calculation.by_fields - combination of values of the processed fields for which statistics were calculated

Example of a JSON Result Object

{
"_index": "repeating_policy",
"_id": "HP9MmY4BcdU8iNUUlvMz",
"_score": 8.92765,
"_source": {
  "_meta": {
    "calculation": {
      "id": "oTHfW44BwooGBkrZbNg_",
      "type": "repeating"
    },
    "execution": {
      "start_time": "2024-04-01T10:55:16.761Z",
      "id": "DP9MmY4BcdU8iNUUlfMZ"
    },
    "object": {
      "identity": [
        "smith.a@saf.com",
        "1456278811",
        "smith.a"
      ],
      "id": "9186db972bafeafed6411ab644d0313bb1def204"
    }
  },
  "_calculation": {
    "extended_stats": {
      "count": 25,
      "min": 4,
      "max": 62,
      "avg": 47.24,
      "sum": 1181,
      "sum_of_squares": 58917,
      "variance": 125.06239999999991,
      "variance_population": 125.06239999999991,
      "variance_sampling": 130.27333333333323,
      "std_deviation": 11.183130152153282,
      "std_deviation_population": 11.183130152153282,
      "std_deviation_sampling": 11.413734416628644,
      "std_deviation_bounds": {
        "upper": 69.60626030430657,
        "lower": 24.873739695693438,
        "upper_population": 69.60626030430657,
        "lower_population": 24.873739695693438,
        "upper_sampling": 70.0674688332573,
        "lower_sampling": 24.412531166742713
      }
    },
    "percentiles": {
      "values": {
        "1.0": 4,
        "5.0": 32,
        "25.0": 44,
        "50.0": 49,
        "75.0": 53,
        "95.0": 61,
        "99.0": 62
      }
    },
    "span": "1h",
    "by_fields": {
      "computer_name": "Lenovo V15"
    }
  }
}
}