Methods and systems for associating internet devices

A system comprising:

• one or more hardware computing device processors;
• one or more memory systems comprising code, executable by the one or more hardware computing device processors, and configured to:
• receive, from a data source, raw or preprocessed data,
• process the raw or preprocessed data received from the data source, thereby resulting in processed data,
• filter, using a data filter, the processed data, thereby resulting in filtered data,
• normalize and sample, using a normalization and sampling operation, the processed data or the filtered data, thereby resulting in output data,
• transmit the output data to a data store,
• generate, using a connectivity generator, a connectivity overlay,
• receive first data from the data store,
• generate, using an event access control operation, an event set, wherein the event set is based at least in part on the first data received from the data store and at least one first rule,
• determine, using a machine learning model, at least one candidate computing device pair, wherein the at least one candidate computing device pair is based at least in part on a first computing device activity history associated with a first computing device comprised in the at least one candidate computing device pair, and a second computing device activity history associated with a second computing device comprised in the at least one candidate computing device pair,
• apply at least one second rule to the at least one candidate computing device pair, and
• generate, based on applying the at least one second rule to the at least one candidate computing device pair, one or more feature vectors associated with the at least one candidate computing device pair,
• wherein a first feature vector comprised in the one or more feature vectors associated with the at least one candidate computing device pair comprises a first confidence result and a second confidence result, the first confidence result being used to generate an inter-device graph, and the second confidence result being used to generate an intra-device graph,
• wherein the machine learning model is trained based on a set of confidence results associated with a set of candidate computing device pairs, wherein a third confidence result comprised in the set of confidence results is mapped to a first candidate computing device pair comprised in the set of candidate computing device pairs.

A method comprising:

• using one or more hardware computing device processors to:
• receive, from a data source, raw or preprocessed data,
• process the raw or preprocessed data received from the data source, thereby resulting in processed data,
• filter, using a data filter, the processed data, thereby resulting in filtered data,
• normalize and sample, using a normalization and sampling operation, the processed data or the filtered data, thereby resulting in output data, and
• transmit the output data to a data store;
• using a connectivity generation system to generate a connectivity overlay;
• using an event access control system to:
• receive first data from the data store, and
• generate an event set, wherein the event set is based at least in part on the first data received from the data store and at least one first rule;
• and
• using a feature vector generation system to:
• determine, using a machine learning model, at least one candidate computing device pair, wherein the at least one candidate computing device pair is based at least in part on a first computing device activity history associated with a first computing device comprised in the at least one candidate computing device pair, and a second computing device activity history associated with a second computing device comprised in the at least one candidate computing device pair,
• apply at least one second rule to the at least one candidate computing device pair, and
• generate, based on applying the at least one second rule to the at least one candidate computing device pair, one or more feature vectors associated with the at least one candidate computing device pair,
• wherein a first feature vector comprised in the one or more feature vectors associated with the at least one candidate computing device pair comprises a first confidence result and a second confidence result, the first confidence result being used to generate an inter-device graph, and the second confidence result being used to generate an intra-device graph,
• wherein the machine learning model is trained based on a set of confidence results associated with a set of candidate computing device pairs, wherein a third confidence result comprised in the set of confidence results is mapped to a first candidate computing device pair comprised in the set of candidate computing device pairs.