Abstract: The disclosure relates to methods and systems of automatically generating rules based on an evolutionary algorithm. A system may generate an initial population of rule candidates bound by a respective constraint. The system may perform a crossover of the initial population of rule candidates with one another to generate a plurality of crossover child rules and may apply a probabilistic mutation to at least some of the plurality of crossover child rules. The system may evaluate performance of the plurality of crossover child rules based on one or more fitness evaluation metrics that measure performance of a given crossover child rule and select a subset of the plurality of crossover child rules based on the evaluated performance. The system may repeat the process until a specified number of evolutionary generations is reached.

Abstract: Systems and methods are provided for identifying and blocking network interactions. One example computer-implemented method includes accessing, by a server computing device, access data associated with an interaction between a user and a virtual location of a first party, where the access data includes a common data element specific to the interaction and at least one click behavior, and accessing identity data associated with the interaction between the user and the virtual location, where the identity data includes the common data element specific to the interaction and identifying data associated with the user. The method then includes joining the access data and the identity data based on the common data element, generating a score based on the joined data and a model, where the sore is indicative of a probability of a type of the user, and reporting the generated score to the first party.