Abstract: The present disclosure provides techniques for extracting structural information using machine learning. One example method includes receiving electronic data indicating one or more pages, constructing, for each page of the one or more pages, a tree based on the page, wherein each level of the tree includes one or more nodes corresponding to elements in a level of elements in the page, encoding, for each page of the one or more pages, a value of each node of the tree for the page into a vector using a first machine learning model, sampling a plurality of pairs of vectors from the one or more trees for the one or more pages, wherein a given pair of vectors corresponds to values of nodes in a same tree, training a second machine learning model using the plurality of pairs, and combining each vector with weights of the second machine learning model.

Abstract: A method identifying clusters with anomaly detection. The method includes aggregating a set of events, of a user, to generate a user vector in response to identifying an event of the set of events. The method further includes aggregating a set of user vectors to a periodic vector for a time period. The method further includes processing a set of periodic vectors to generate a periodic distance. The method further includes selecting the time period, corresponding to the periodic vector, using the periodic distance and a threshold. The method further includes processing the set of user vectors to generate clusters of user vectors, wherein the set of user vectors includes the event during the time period. The method further includes processing the clusters of user vectors to identify a selected cluster and performing an action to a set of user accounts corresponding to the selected cluster.

Abstract: Techniques are disclosed relating to assessing technology activity using image-based machine learning algorithms. A computer system may access a data set that includes a plurality of parameters (e.g., technologies) for an item (e.g., a web-based interface). The plurality of parameters may correspond to a plurality of time intervals. The computer system may generate a two-dimensional graphical representation of the data set. A first dimension of the graphical representation may be indicative of values of the plurality of parameters at different time intervals and a second dimension of the graphical representation may be indicative of a time period that includes the plurality of time intervals. At least one characteristic of the data set may be determined by inputting the graphical representation of the data set to a trained machine learning module. The trained machine learning module may implement an image-based learning algorithm.

Abstract: Feature randomization for securing machine learning models includes receiving an event, and altering, responsive to receiving the event, a threshold pseudo-randomly to generate an altered threshold value. Feature randomization further includes applying the altered threshold value to a threshold-dependent feature to generate an altered threshold-dependent feature value. The altered threshold-dependent feature value determined at least in part from the event. Feature randomization further includes executing a machine learning model, on the event and the altered threshold-dependent feature value, to generate a predicted event type for the event.

Abstract: Techniques are disclosed relating to assessing technology activity using image-based machine learning algorithms. A computer system may access a data set that includes a plurality of parameters (e.g., technologies) for an item (e.g., a web-based interface). The plurality of parameters may correspond to a plurality of time intervals. The computer system may generate a two-dimensional graphical representation of the data set. A first dimension of the graphical representation may be indicative of values of the plurality of parameters at different time intervals and a second dimension of the graphical representation may be indicative of a time period that includes the plurality of time intervals. At least one characteristic of the data set may be determined by inputting the graphical representation of the data set to a trained machine learning module. The trained machine learning module may implement an image-based learning algorithm.