Inspiration
Our inspiration for this project was the many spam or fraudulent emails people come across. Across the world, some company employees receive an average of 49 emails per year per employee that is fraudulent. One example of such an attack was on a gas pipeline in Florida, the result of this hack was a rise in gas prices in the area during the period of the attack and a 4.4 million dollar ransom. This was a big problem that we hoped to solve.
What it does
Our software is a software extension that uses machine learning to detect whether an email sent is fraudulent and if so, it makes sure to make it and thus inform the user that such a result has occurred.
How we built it
We built it using a NextJS Landing Page, React Frontend and Golang Backend. The Golang Backend handled custom built authentication and handing Machine Learning Requests. The NextJS Landing page invited users to the extensions and prompted them to install the extension. The React Frontend modal allowed users to receive information about emails and sign-in of necessary
Challenges we ran into
Golang Errors (it was fairly new), Using React with Chrome Extensions (completely new) and doing Machine Learning with Co:here
Accomplishments that we're proud of
Completing the application!
What we learned
Co:here for Machine Learning and Golang for the Speedy Backend.
What's next for CyberEmail
Extending support for other mail services and adding new Machine Learning models to enhance the User Experience.
Co:here
Co:here was a wonderful sponsor as it helped us build our different machine learning models. It was an easy process to build our machine learning models and the accuracy that came out of the co:here models were fantastic
Github
GIthub made it a breeze to host our software development source code onto a cloud platform to share it to the world in a way that made coding more accessible to people and overall helped the hacker community (a clear goal both MLH and Github).
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