Attackers appear virtually identical to genuine users by hijacking their devices, simulating human behavior, and leveraging stolen identities. Their tooling and methods change rapidly, so it is extremely difficult for the naked eye alone to tell the difference between real and fake without the assistance of machines.
For a neural network to be useful in discerning actors that are actively trying to fool it, the system must be trained on billions of requests until the inputs are so precise that they can definitively assess the correct answer every time, no matter the sophistication of the attackers. In other words, the neural network has taught itself what a human request looks like.
Shape has defended the world’s largest companies for years, giving our machine learning models access to data that comprehensively resembles the real-world of attackers and their ability to retool. Today, Shape processes over 500 million transactions per day, including 50 million new attacks and 100 million real human logins every day.