Soon after the first wave of fraud applications were identified in June 2016, the CCC Technology Center took immediate steps to strengthen the security of the CCCApply system and protect our students' personal identifiable data (read more about all the ways we are addressing fraud in CCCApply). Meanwhile, we contracted with a machine learning data research team to perform data analysis on several thousand fraud applications examples that were collected from the colleges that initially reported the spam.

Research Objectives

The objectives for the research project were simple:

  • Understand why we are seeing an influx of fraudulent applications across the CCC system
  • Understand the motivations behind these fraudulent attacks
  • Identify trends, commonalities and patterns in the data
  • Identify the tools and techniques being used by spammers
  • What can CCCApply do to prevent fraud now and in the future?
  • What can the colleges do to prevent fraud now and in the future?


Additional objectives were added based on the recommendations and outcomes of the research, including commencing a small pilot of four colleges to get feedback and better understand their workflow processes, as well as develop a process for collecting data throughout the design and development phase of the project. 

Data Analysis

Based on what they learned in the initial review, the research team conducted a multi-part data analysis of all submitted applications (without using any student personal information). In the first review, the focus was on one college that provided a large number of bad applications between June 1, 2016 - August 15, 2017.  The second review looked at all other colleges who provided examples of bad applications in the same time period; and the third pull looked at all remaining colleges' submitted application data. It was important to compare the bad applications to good applications in order to start detecting trends and patterns in the fraudulent "formula".

After reviewing all three data pulls, even without including personal identifiable information, we learned a great deal.

The majority of bad applications identified were submitted in under 3 minutes, with the majority of those being submitted in under 2.5 minutes. This information alone told us that robots are likely submitting applications using keyboard strokes;

Of the applications identified as frauds, other patterns were prevalent:

Recommendations

By identifying commonalities across all the fraud applications submitted by colleges - such as volume, average submission time, patterns in the submitted data, and user profiling - and then comparing that information to non-fraud applications, the research team was able to make some high-level recommendations, including short-term fixes and long-term solutions, that we could start implementing immediately. The recommendations included:

These recommendations were all approved as part of an overall enhanced security strategy for 2018-2019. 

Spam Pilot Project

One of the recommendations from the research study was to organize a small pilot of colleges that can work with our support engineers and provide feedback throughout the research and development efforts. The pilot colleges will also collaborate on best practices and other workflow changes that can be shared back with the other colleges.

enhancements to help prevent fraud applications from getting back to the colleges through their download system to prevent bad data from getting to the colleges and continuously re-training the prediction service model. 

Development Project

One of the recommendations from the research develop a spam filter web service that would prevent these the bad applications from getting back to the colleges through their download system to prevent bad data from getting to the colleges and continuously re-training the prediction service model. 


After the initial review, the data analysts recommended developing a spam filter service using on a continuous learning/training model - based on a custom algorithm that will get smarter each time an application is flagged as "spam". This filter service is being built for CCCApply Standard application, with a back-end user interface that will be accessible in the new CCCApply Administrator (deploying in June). Both the spam filter service and the admin interface are under-development now - with an expected release date of June 2018. This is a huge project and will require the cooperation and participation of all colleges - not just the colleges being targeted with spam - in order to "train" the algorithm with accurate data - both good, legitimate applications as well as the bad, fraudulent applications.  


Spam Filter Web Service 

One of the outcomes of the research study was the recommendation to develop a spam filter web service that would prevent these the bad applications from getting back to the colleges through their download system to prevent bad data from getting to the colleges and continuously re-training the prediction service model. 


Meanwhile, we continue to work with the machine learning team and several colleges in a pilot project to build and train the algorithm with any bad applications submitted by colleges.  The email tomorrow will also specify how colleges can submit their fraud applications to the Tech Center for this purpose (we need them formatted in a specific way and ensure colleges know not to include any student personal identity information.

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

Lessons Learned: Motivations 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. 
We've found that the majority of spammers are seeking financial gain and are targeting colleges that are giving away something for free at the time of application - specifically, .edu email addresses, as well as free software licenses -before the applicant has been officially admitted to the college (registered, or other vetting process). 
Among other things, in appears these spammers are using the .edu email addresses to:  
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

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.