How I want the spamming scammers to feel
Architecture of the solution
Desktop utility code.
The first microservice which maintains the list of potential as well as confirmed spam emails
The second microservice which displays which fake usernames and passwords have been used where.
The third microservice which has manual review capabilities and would also include image analysis to determine the company impersonate
I got three scam emails in the first hour of this hackathon and wanted a way to be rid of them. Traditional filtering is still letting stuff through.
What it does
It is a privacy first way to send massive numbers of auto generated emails to scammers who are trying to prey on people. The goal is to overwhelm their capacity to read and respond to all of them and encourage them to find something else to do with their time and technology.
How I built it
It uses three microservices along with a desktop client. 2 of the microservices are in Django and the 3rd is in Flask. The services are hosted on Microsoft Azure while using Google NLP and Storage for the analysis and screenshots respectively.
Challenges I ran into
After deploying two microservices on Azure, it is really demotivating to deploy on Google Cloud. Azure nicely integrates Git, CI, and deployment.
Accomplishments that I'm proud of
Got all my various APIs to play nice with each other.
What I learned
How to build and integrate microservices, at least with most of the security turned off.
What's next for ScamClogger
We shall see!