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). |
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Meanwhile, we contracted with a machine learning data research team to perform data analysis on several thousand fraud applications examples that |
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were collected from the colleges |
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that initially reported the spam. Research ObjectivesThe objectives for the research project were simple:
In addition, the research team will work with the CCCApply product manager and support team to commence Additional objectives were added based on the recommendations and outcomes of the research, including commencing a small pilot of four colleges to help get feedback and better understand their workflow processes, as well as develop a process for ongoing collection of data and fraud applications for continuous analysis and disseminate information to the colleges.collecting data throughout the design and development phase of the project. |  |
Data Analysis
Based on that what they learned in the initial review, we initiated the research team conducted a multi-part data analysis of all submitted applications (without using any student personal information). In the first data review, we focused the focus was on one college that provided a large number of bad applications between June 1, 2016 - August 15, 2017; the second analysis . The second review looked at all other colleges who provided examples of bad applications in the same time frameperiod; and the third pull looked at all remaining colleges and ' 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".
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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
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Recommendations
By identifying characteristics common in the fake applications collected 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, we are able to take steps to prevent this threat through enhanced security, the research team was able to make some high-level recommendations, including 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 and long-term solutions, that we could start implementing immediately. The recommendations included:
- Additional security measures to the firewall and expanding our blocks on bad actors and ip addresses,
- Several stop gap configuration changes to the pre-submission process to temporarily stop spammers before they submit
- Implement a pilot with a few colleges that are getting spammed to help develop best practices and other prevention tactics to share with all colleges
- Develop a machine learning algorithm based on a continuous learning/re-training model to filter fraud before it reaches the colleges
These recommendations were all approved as part of an overall enhanced security strategy for 2018-2019.
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Spam Pilot Project One of the outcomes of recommendations from the research study was the recommendation to develop 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.
Research Outcomes
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.
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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?
Research Outcomes: What We've Learned
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Lessons Learned: Motivations for Fraudulent Activity
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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).
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