Software with a location-based license agreement and which require one user per license often face difficulty verifying the authenticity of the end user. Since the license agreement requires the user to have one license per user per location, often times technology can be exploited to spoof the location or have multiple users under the same license.

What it does

Our technology is focused on preventing just that. We provide multiple layers of smart authentication, network and location detection. This prevents the user from using the software under the specified license agreement. If our system detects a license breach it uses smart reasoning and machine learning to authenticate the user so the user faces minimal inconvenience. Our system keeps track of the user's IP address and when it changes. It calculates the time between the IP change to intelligently make a decision whether the user is at the same location or not. It also uses facial recognition and smart environment detection to verify that the user is themselves and the software is only being used by the one under which it is licensed.

How we built it

We used Azure and Google cloud services to train our machine learning models. Then, we used python to call the API's we created and then bundled our algorithms in a GUI tool also using python. Furthermore, we also have a business website for or product information

Challenges we ran into

Trying to decide between going with a web app or python GUI. We chose the latter as it can be more easily integrated with already existing downloadable or online software.

Accomplishments that we're proud of

We are proud of being able to successfully get our facial recognition working.

What we learned

We learned about machine learning and how powerful APIs can be.

What's next for KoalityAuth

We are trying to integrate smart voice recognition using the Google Cloud Platform and add as many security layers as possible. Our main goal is to ensure the authenticity of the user and prevent any kind of interdependent license breach.

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