Abstract: Disclosed herein are techniques for generating embedded data for cursor-trail data including identifications of sequential positions of a cursor. A method described herein involves obtaining cursor-trail data identifying sequential positions of the cursor. The method further includes using a generative machine learning model to generate an embedding of the cursor-trail data. The generative machine learning model was previously trained using a machine learning model. The training using the machine learning model was unsupervised training including generating embeddings of training cursor-trail data, generating predicted cursor-trail data using a decoder neural network, and comparing the predicted cursor-trail data to the training cursor-trail data to determine a loss function for refining the generative machine learning model.

Abstract: Disclosed herein are techniques for detecting invalid web traffic. A method described herein involves obtaining an input sequence. The method further includes inputting the input sequence to an encoder model configured to generate an embedding from the input sequence. The method includes generating, as an output of the encoder model, an embedding. The method includes inputting the embedding to a classifier model configured to predict a vector of probability values corresponding to a plurality of classes using the embedding. A first class of the plurality of classes may be attributed to variable traffic and a second class of the plurality of classes may be attributed to repetitive traffic. The method includes predicting, as an output of the classifier, the vector of probability values. The method includes determining a machine-actor score from the vector of probability values. The method also includes outputting the machine-actor score.