5 Stages Of Machine Learning For Identity Authentication

Ty Chaston
March 11, 2019

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1984 was the year George Orwell predicted a dystopian future that didn’t actually manifest, but that is also the year that gave rise to a different fear. The fear of robots taking over the Earth and killing all humans was the premise of “The Terminator” and exposed millions to the concept of thinking machines and the destructive power of learning machines.

What Is Machine Learning?

While you may have preconceived notions of the perils of learning machines from Hollywood movies, the actuality is somewhat more boring. According to TechTarget Machine Learning(ML) is: 

“a category of algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available.”

The article goes on to describe how the process actually works: 

“The processes involved in machine learning are similar to that of data mining and predictive modeling. Both require searching through data to look for patterns and adjusting program actions accordingly. Many people are familiar with machine learning from shopping on the internet and being served ads related to their purchase.”

To truly demystify ML, we should dissect this process to its core stages. As it relates to identity authentication it is not unlike the process your credit card company uses to determine if potential fraud is occurring while someone is using the card. For identity authentication, the process is more thorough and tracks more behaviors.

5 Stages Of Machine Learning

Like most processes, ML has a series of stages that enable it to be more successful with the algorithm’s final predictions. Specifically, you can separate out these five stages:

  1. 1) Scene modeling: In this is the primary stage, the algorithm sets the boundaries, specifically the objectives and constraints, for analysis and modeling. (i.e. what are we analyzing?) In the case of identity authentication this encompasses the digital world of the individual being authenticated. NOTE: The algorithm doesn’t know the identity of that individual (i.e. John Smith), just that a hashed key represents access to the model for this individual
  2. 2) Detection: In this stage, the algorithm discerns different patterns in order to detect the parameters that define specific objects in the model. (i.e. a catalog of items in the model) In the case of identity authentication this encompasses identifying the differences between specific cyber applications, and hardware being used by the individual. NOTE: no one actually has access to any of this data as everything is encrypted in transit and at rest and can’t be traced back to the individual.
  3. 3) Recognition: In this stage, the algorithm identifies the patterns detected above as specific items. (i.e. what each item in the catalog represents) In the case of identity authentication this encompasses the understanding of the expected characteristics of the cyber applications and hardware to create an ongoing baseline for this individual.
  4. 4) Inference: In this stage, the algorithm infers specific usage for the items recognized in the last stage (i.e. what the usage is for each item) In the case of identity authentication this encompasses measuring the unique patterns/behaviors of use of the cyber applications and hardware to improve the ongoing baseline for this individual.
  5. 5) Prediction: In this final stage, the algorithm makes decisions based on modeled expectations   (i.e. what the item should do next) In the case of identity authentication this encompasses comparing the current pattern/behavior to the unique patterns/behaviors of use inferred above to determine if this is actually individual authenticated.

Now that you know ML isn’t is scary as The Terminator would have you believe, let’s look at an application that can protect your identity authenticating into all of your cyber locations.

AIML-Based Authentication

Acceptto’s eGuardian engine continuously creates, and monitors user behavior profiles based on the user interaction with the It’sMe authenticator. Every time an activity occurs, actionable intelligence is gathered and used to optimize the user profile. eGuardian is capable of autonomously and continually learning new policies and adapting existing ones. While policies can still be manually defined and contribute to the computation, our Biobehavioral AIML approach automatically finds the optimal policy for each transaction. eGuardian leverages a mixture of AI & ML, expert systems and SMEs to classify, detect, and model behavior, and assign real-time risk scores to continuously validate your identity prior to, during and post-authentication. 

Check out what Acceptto can do to ensure your employees, partners and customers can authenticate without passwords and still ensure security and privacy registering for a free demo today.


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