The go-to solution was to use Docker Images. We also needed to be sure that the solution would work for our development environment alongside our validation and our production platforms. We only needed to analyse the stream of the files. The backend was mainly designed as a proxy for all the requests that the frontend wanted to make with the external API, which means we would not be storing any of the files uploaded by the customer. Our stack was a React frontend and a Django Backend, hosted on AWS Elastic Beanstalk.
We had a few requirements for the files to be valid and one of them was to ensure they were checked for any virus before posting their content to the API. Not only are the patterns content agnostic, they are also language-independent making it equally effective for all message formats and encoding types.I recently had to allow customers to upload files on a website, then send their content to an external API.
RPD identifies incoming threat messages with very few, if any false positives. RPD is designed to distinguish between the distribution patterns of solicited bulk emails, representing legitimate business correspondence, from those of unsolicited bulk emails, representing spam. These global data center queries then update the cache to keep the most current patterns available locally. If a pattern is not in the local cache, RPD will query global data centers for a match to provide true real-time protection with the most up-to-date patterns. RPD patterns are compared to the local cache which provides fast match results for more than 70% of the patterns. It classifies both distribution and structural patterns within an email message. RPD extracts and then analyzes relevant patterns, which are used to identify massive email-borne outbreaks. These patterns cannot be used to reconstruct the original message. Recurrent Pattern Detection (RPD) uses patented technology to identify patterns which are content agnostic to safeguard the original message. 
Recurrent Pattern Detection™ (RPD) Technology
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