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We are also working with the CCCApply Steering Committee to better understand the motivations of these spammers. What are they after? 


Machine Learning Research Study

Infiniti commenced a multi-phase research project with the following objectives: 

  • To compile the data and do exploratory data analysis
  • To identify trends and patterns in the incoming data 
  • To identify tools and techniques used by spammers
  • To better understand the motivations by spammers


Research Outcomes

Data Trends Identified in Fraud Applications

By recognizing the characteristics of spam applications, such as volume, average submission time, patterns in the submitted data, and user profiling - and comparing that information to non-fraud applications, we are able to take steps to prevent this threat through enhanced security, short-term stop gap fixes as needed, and the development of a spam filter web service. These aren't the only solutions, but as we continue to better understand the motivations behind these attacks, these can be used as part of an overall enhanced security strategy.




Early Research

In order to better understand trends and patterns within these fraud applications so thaThe Tech Center has contracted with a Machine Learning Research organization to better understand the make-up of fraud applications. In order to combat these frauds, we have to be able to identify them as they are coming in. To start we've pulled data for a two-part data analysis (without using any student personal information): the first data pull focused on one college that provided a large number of bad applications between June 1, 2016 - August 15, 2017; the second data pull looked at all other colleges who have provided examples of bad applications in the same time frame; and the third pull looked at all the remaining colleges and submitted application data. We need to compare the bad applications to good applications in order to start detecting trends and patterns in the fraudulent "formula".

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  • Time to completion:  2.25 minutes (average)
  • Permanent Address State: NOT California
  • Current Mailing Address State:  NOT California
  • Gender: Male
  • Race: White
  • HS Ed Level:  No high school completion
  • Interest in Financial Aid:  NO


Research Outcomes: What We've Learned

Trends & Motivation for Fraudulent Activity

We've identified several motivating factors and are working with our security office to publish some best practices to help colleges prevent bad applications from being submitted in the first place. 

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To confirm our suspicions, we surveyed the colleges that have reported fraudulent applications and each one of the colleges confirmed that they have been giving new applicants a .edu address automatically upon application submission. 

Other Motivating Factors

  • Some colleges are giving applicants free software licenses (Office 365). These licenses are being sold to end-users.
  • In some instances, confirmation emails being sent to applicants are confirming their residency status (based on self-reported data). These are then being used to create fake identities.
  • Student ids and other "identification codes" are allowing these fraud applicants to access the colleges' SIS (again, this is happening prior to registration).

From a security standpoint, allowing students to access a college's student information system prior to registration or matriculation process is a high risk that our Chief Security Officer, Jeff Holden, is also investigating to see what can be done from a systemwide perspective. 

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