Machine learning-based anomaly detection for human presence verification

A computer program product, comprising a tangible machine-readable storage medium having encoded therein executable code of one or more software programs, wherein the one or more software programs when executed by at least one processing device perform the following steps:

• obtaining data from a plurality of data sources associated with a monitored location for storage into a data repository;
• processing the data to generate substantially continuous time-series data for a plurality of distinct features within the data;
• applying the substantially continuous time-series data for the plurality of distinct features to at least one machine learning baseline behavioral model to obtain a probability distribution representing a behavior of the monitored location over time;
• and
• evaluating a probability score generated by the at least one machine learning baseline behavioral model to identify an anomaly at the monitored location.

An apparatus, comprising:

• a memory;
• and
• at least one processing device, coupled to the memory, operative to implement the following steps:
• obtaining data from a plurality of data sources associated with a monitored location for storage into a data repository;
• processing the data to generate substantially continuous time-series data for a plurality of distinct features within the data;
• applying the substantially continuous time-series data for the plurality of distinct features to at least one machine learning baseline behavioral model to obtain a probability distribution representing a behavior of the monitored location over time;
• and
• evaluating a probability score generated by the at least one machine learning baseline behavioral model to identify an anomaly at the monitored location.