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Oracle® Fusion Middleware Administrator's Guide for Oracle Adaptive Access Manager
Release 11g (11.1.1)

Part Number E14568-06
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19 Predictive Analysis

Oracle Adaptive Access Manager's Predictive Analysis feature compliments configurable rules and behavioral profiling by enabling you to perform statistical risk analysis in real time using its out-of-the-box predictive analytic application that integrates ODM features, such as data mining and data analysis algorithms. Risk analysis is trained over time.

This chapter contains the following sections:

19.1 Important Terms

Important terms for predictive analysis are presented in this section.

19.1.1 Predictive Analysis

Predictive analytics encompasses a variety of techniques from statistics, data mining and game theory that analyze current and historical facts to make predictions about future events.

  • Individual User Behavior Profiling: End user login behaviors are evaluated to determine how abnormal it is currently compared to their own past behavior, if there is past behavior captured.

  • Individual Device Profiling: Devices used for login have behavior that is evaluated to determine how abnormal it is currently compared to their own past behavior if past behavior has been recorded.

  • New Device Profiling: If a device does not have any historical data to profile then predictive techniques are used to determine how risky the device is.

  • User Type and Location Profiling: Predictive models evaluate the degree of anomaly based on the type of user (groups, Organization ID) rather than each individual user.

  • User Type and Time Profiling: Similar to location profiling, time profiling uses predictive techniques to identify anomalies in behavior when there is not much historical data for the specific user but there is production data related to users of the same type.

19.1.2 Data Mining

Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis. Data mining uses sophisticated mathematical algorithms to segment the data and evaluate the probability of future events. Data mining is also known as Knowledge Discovery in Data (KDD).

Data mining can answer questions that cannot be addressed through simple query and reporting techniques.

19.1.3 ODM

Oracle Data Mining (ODM) is an option that extends Oracle Database 11g Enterprise Edition's out-of-the-box capabilities. ODM implements data mining and data analysis algorithms for prediction and anomaly detection and enables deployment of data mining models inside the database. The ODM option is not a separate component; functionality is built into the Oracle database kernel and operates on data stored in the database tables. There is no need to move data out of the database into files for analysis and then back from files into the database for storing. The data never leaves the database -- the data, data preparation, model building, and model scoring results all remain in the database.

19.1.4 Predictive Models

Predictive models are supervised learning functions. Using predictive models, OAAM fine tunes its analysis; the more each model is trained, the more accurate the risk analysis becomes. The out-of-the-box predictive models are trained in two ways: the anomaly detection model trains automatically when fed historical access data, and the fraud classification model trains on the findings of human fraud investigators. You can configure additional models as required to meet specific deployment use cases. This approach to predictive risk analysis allows you to clearly see on which decisions outcomes are based and allows augmentation as required.

19.2 Prerequisites

Make sure the following prerequisites are met before you activate the Predictive Analysis functionality:

19.3 Initial Setup

  1. Create an ODM database user. Execute the SQL script create_odm_user.sql.

    When it prompts for inputs, enter the ODM user name as the value of first parameter and then the password of ODM User as the value of second parameter.

    The script is located in the $MW_HOME\oaam\cli\odm folder.

  2. Set up the OAAM CLI environment. Make sure you have added the following to the CSF/Credential Store using Enterprise Manager:

    1. OAAM DB User Name and Password with oaam_db_key as the keyname under the map oaam.

    2. ODM DB User Name and Password with oaam_odm_db_key as the keyname under the map oaam.

    3. Set the property oaam.db.url with the JDBC URL of the OAAM database in

  3. By default Predictive Risk uses the OAAM_CLASSIFIED_REQUEST_VIEW. For predictive risk to work for sessions from non-flash devices you need to use "OAAM_CLASSIFIED_REQ_NOFLASH_VW".

    OAAM_CLASSFIED_REQ_NOFLASH_VW view has all the requests (both flash and no-flash).

    To set the OOTB ODM Model "OAAM Fraud Request Model" to use the no-flash data set the following properties before running

  4. Run the shell script in the OAAM CLI folder. This script does the following:

    • Seeds the ODM tables that have the normalized data of the browser and flash fingerprints



    • Creates the following database views that are used as input data by the ODM models:






    • Creates the following ODM Models if required data is present:



  5. Log in to OAAM Admin Server and link the Predictive Analysis Policy to All Users or the required user groups.

  6. Log into WebLogic Admin Server using the WebLogic Console and create a DataSource with JNDI name such as jdbc/OAAM_SERVER_ODM_DS and point it to the ODM DB User and add the Managed server of OAAM Server as the target.

  7. Restart OAAM Server since ODM initialization updates some enum-related properties.

  8. To test anomaly detection, try to log in from a different kind of browser or location which is not yet present in the OAAM database.

  9. To test "fraudulent session prediction" functionality, log in in a similar session that is linked to an Agent case which is closed with the Confirmed Fraud disposition.


By Default Predictive Risk uses the OAAM_CLASSIFIED_REQUEST_VIEW. For predictive risk to work for sessions from non-flash devices you need to use "OAAM_CLASSIFIED_REQ_NOFLASH_VW".

OAAM_CLASSFIED_REQ_NOFLASH_VW view has all the requests (both flash and no-flash).

To set the OOTB ODM Model "OAAM Fraud Request Model" to use the no-flash data set the following properties and run


19.4 Rebuild the ODM Models to Provide Feedback and Update Training Data

Important points about rebuilding the ODM models are presented in this section.

19.5 Policy Evaluation

The following steps describe the flow of Predictive Analysis evaluation:

  1. OAAM User Request goes for Post-Authentication checkpoint evaluation.

  2. Predictive Analysis policy executes as part of Post-Authentication.

  3. The Check if the current request is fraudulent rule is executed. As part of the execution it takes the required classification type and values of attributes from current request and executes the ODM SQL function prediction_probability() with the given model name. This call returns a prediction probability value which is tested to see if it falls in the given range. If so then the OAAM Suspicious Fraudulent Request alert is generated and risk score is set to 1000.

  4. The Check if the current request is anomalous rule is executed. As part of the execution it takes values of attributes from current request and executes the ODM SQL function prediction_probability() with the given model name. This call returns a prediction probability value which is tested to see if it falls in the given range. If so then the OAAM Anomalous Request alert is generated and the risk score is set to 1000.

19.6 Tuning the Predictive Analysis Rule Conditions

The following parameters of Predictive Analysis rule conditions can be tuned/changed:

To set the above parameters you can go to the Predictive Analysis Policy and navigate to the required rule and update the parameters.


The following sections describe advanced functionality which is typically performed by integrators who have Java coding knowledge and knowledge of both OAAM and ODM.

19.7 Adding Custom Database Views

19.8 Adding Custom Grants

19.9 Adding New ODM Models

To add a new ODM Model, follow these steps:

  1. Determine the type of model. Currently OAAM supports only CLASSIFICATION models.

  2. Determine if the existing ODM view can be used to build the model. If not, create a new view and add that definition to $MW_HOME\oaam\cli\odm\custom_oaam_odm_views.sql.


    Make sure the view definition SQL ends with ";" and there are no extra lines or comments in the file.
  3. Determine if any of your new views require additional grants to access the OAAM tables or any custom tables. Add those custom grants to $MW_HOME\oaam\cli\odm\custom_oaam_grants_to_odm_user.sql.


    Make sure the grant statements end with ";" and there are no extra lines or comments in the file.
  4. Create a new ODM model using Oracle Data Miner or using the SQL command call dbms_data_mining.drop_model(). Refer to ODM documentation for details.

  5. Test your ODM model using sample data. You can typically do this by executing the following:

    • For anomaly detection models:

      Select prediction_probability(<model_name>, '0' using <value1> as attribute1, <value2> as attribute2, …. <valueN> as attributeN> from dual

    • For other classification models:

      Select prediction_probability(<model_name>, <classificationValue> using <value1> as attribute1, <value2> as attribute2, …. <valueN> as attributeN> from dual

  6. Once you are done with testing, add a new enum element to oracle.oaam.odm.model.enum with the following properties:

    Table 19-1 Properties for oracle.oaam.odm.model.enum

    Property Name Notes


    Name of the model


    Description of the model


    Type of the model.

    Anomaly Detection: oracle.oaam.odm.modeltypes.enum.oneclasssvm

    Classification: oracle.oaam.odm.modeltypes.enum.classification


    Exact name of the ODM model. The OAAM setup script uses this to create the ODM model.


    Exact name of the input data table/view name. The model will be built using this table/view name.


    Column in the data table/view that uniquely identifies each row.


    Do not specify this for Anomaly Detection models. For classification models, specify the column whose value has to be predicted. Typically this column should have the values ('fraud' or 'not_fraud') as mentioned in the oracle.oaam.odm.fraud_classification_types.enum'


    Name of the database table that has settings for the ODM model. You can use the existing tables 'OAAM_ANOMALY_MODEL_SETTINGS' for Anomaly Detection models and 'OAAM_ANOMALY_MODEL_SETTINGS' for Classification models if you don't have any explicit settings.


    Specify how the input required for evaluation/scoring is mapped to OAAM Data. You can use the following existing mappings if you do not have any new requirements. Otherwise refer to Section 19.10, "Adding Custom Input Data Mappings":




    Set it as 'false' so that script can build the ODM model and set this value to 'true'. If you already built the ODM model by yourself then set this value to 'true' so that the OAAM rules can use this model to evaluate/score against input data.

19.10 Adding Custom Input Data Mappings

This section contains information about adding custom input data mappings.

19.10.1 When to Use

Custom input data mappings are needed if any of the following conditions apply:

  • You want to use fewer attributes (than what is available out-of-the-box) to evaluate/score the out-of-the-box ODM models

  • You want to create a custom ODM model based on custom table/view that has different set of attributes than the existing input data mappings.

19.10.2 Using OAAM Attributes to Build a Custom Input Data Mapping

You can use existing OAAM attributes and create custom input data mappings. This approach is useful if you are reusing the existing database view that uses OAAM request data that includes session, browser-fingerprint, flash-fingerprint, and location data.

Steps to create an input data mapping are as follows:

  1. Add a new enum element to oracle.oaam.odm.datamapping.enum.

  2. Set the inputdata_mapping property of model enum element to point to the newly added enum element.

  3. Add the required list of name-values from the following list to the newly added enum element:

    • request_minute=request.minute

    • request_hour=request.hour

    • request_day_of_week=request.day_of_week

    • request_day_of_month=request.day_of_month

    • request_day_of_year=request.day_of_year

    • request_week_of_month=request.week_of_month

    • request_week_of_year=request.week_of_year

    • request_month=request.month

    • request_quarter=request.quarter

    • request_year=request.year

    • auth_status=request.auth_status

    • user_identifier=request.user_identifier

    • login_id=request.login_id

    • user_group_id=request.user_group

    • request_ip_address=request.ip_address

    • is_registered=request.is_registered

    • auth_client_type=request.auth_client_type

    • secure_client_type=request.secure_client_type

    • pre_auth_action=request.pre_auth_action

    • post_auth_action=request.post_auth_action

    • device_id=device.device_id

    • device_cookie_disabled=device.cookie_disabled

    • device_flash_disabled=device.flash_disabled


    • browser_language=browser.language

    • browser_language_variant=browser.language_variant

    • browser_name=browser.browser_name

    • browser_operating_system=browser.os

    • browser_user_agent_string=browser.user_agent_string

    • audio_video_disabled=flash_fingerprint.audio_video_disabled

    • has_accessibility=flash_fingerprint.has_accessibility

    • has_audio=flash_fingerprint.has_audio

    • has_audio_encoder=flash_fingerprint.has_audio_encoder

    • embedded_video=flash_fingerprint.embedded_video

    • has_ime_installed=flash_fingerprint.has_ime_installed

    • has_mp3=flash_fingerprint.has_mp3

    • supports_printer=flash_fingerprint.supports_printer

    • supports_screen_broadcast=flash_fingerprint.supports_screen_broadcast

    • supports_playback_screen_brd=flash_fingerprint.supports_playback_screen_brd

    • supports_streaming_audio=flash_fingerprint.supports_streaming_audio

    • supports_streaming_video=flash_fingerprint.supports_streaming_video

    • supports_native_ssl=flash_fingerprint.supports_native_ssl

    • contains_video_encoder=flash_fingerprint.contains_video_encoder

    • debug_version=flash_fingerprint.debug_version

    • flash_language=flash_fingerprint.flash_language

    • is_local_file_read_disabled =flash_fingerprint.is_local_file_read_disabled

    • manufacturer=flash_fingerprint.manufacturer

    • flash_operating_system =flash_fingerprint.flash_operating_system

    • aspect_ratio_of_screen =flash_fingerprint.aspect_ratio_of_screen

    • player_type=flash_fingerprint.player_type

    • is_color_screen=flash_fingerprint.is_color_screen

    • dots_per_inch=flash_fingerprint.dots_per_inch

    • screen_resolution=flash_fingerprint.screen_resolution

    • flash_version=flash_fingerprint.flash_version

    • country_id=location.country_id

    • state_id=location.state_id

    • city_id=location.city_id

    • metro_id=location.metro_id

    • isp_id=location.isp_id

    • routing_type=location.routing_type

    • connection_type=location.connection_type

    • connection_speed=location.connection_speed

    • top_level_domain=location.top_level_domain

    • sec_level_domain=location.secondary_level_domain

    • asn=location.asn

    • carrier=location.carrier

    • zip_code=location.zip_code

    • region_id=location.region_id

    • phone_area=location.phone_area

19.10.3 Using Custom Attributes to Build a Custom Input Data Mapping

If you want OAAM to use custom attributes while evaluating/scoring an ODM model then you can develop custom java class that can be used to get values of the custom attributes.

Follow these steps to use custom attributes for building and evaluating ODM models

  1. Add a new enum element to 'oracle.oaam.predictive_analysis.attribute_resolvers.enum'.

  2. Add 'class' property with value as the fully qualified class name of the Java class that will have logic to return values for the custom attributes.

  3. Add all the custom attributes as properties to the newly added enum element. Value of these properties can be the name/description of the attribute. Do not use 'name', 'description', 'class' as attribute names.

  4. Develop the custom Java class that handles custom attributes.

    • It should extend the OAAM class oracle.oaam.integration.datamining.rules. OAAMAttributesResolver

    • It should implement a public constructor that takes requestId as the parameter. That constructor should call the super constructor.

    • It should extend the method public Object getValue(String attributeName) and have logic to return the value of given attribute. AttributeName will be in the format of '<enumElement>.<property>'

    • Deploy the custom Java class as an OAAM Extension using OAAM Extensions Shared Library. Refer to Developer Guide for deploying OAAM Extensions.

  5. If you are using a custom database view then add a custom mapping by adding new enum element to 'oracle.oaam.odm.datamapping.enum' enum and add all the column names of the database view as properties to this enum element. Add the related custom attribute name as the value for these properties. Value should be in the format of <enumElement>.<property>.

  6. If you are not using custom database view but just want to create custom mapping of existing request data then pick the required columns from the following and add them to your custom mapping enum element:

    Table 19-2 Custom Mapping

    A B C