Human or bot activity detection
Systems, methods, apparatuses, and computer program products for human or bot activity detection. The method may include, collecting time-series data on one or more events occurring on a webpage. The method may also include deriving classifications of the one or more events. The method may further include performing functional transformations of the time-series data. In addition, the method may include determining potential features of the one or more events based on a combination of the classifications of the one or more events, and results of the functional transformation. Further, the method may include training a machine learning model with the potential features. The method may also include determining, via the machine learning model, bot behavior and non-bot behavior of the one or more events.
1. An apparatus, comprising:
at least one processor; and
at least one memory comprising computer program code,
the at least one memory and the computer program code are configured, with the at least one processor, to cause the apparatus at least to
collect time-series data on one or more events occurring on a webpage;
derive classifications of the one or more events;
perform functional transformations of the time-series data;
determine potential features of the one or more events based on a combination of the classifications of the one or more events, and results of the functional transformation, wherein the classifications of the one or more events comprise bot traffic and human traffic, and wherein the results of the functional transformation comprise numeric data or categorical data;
train a machine learning model with the potential features;
determine, via the machine learning model, bot behavior and non-bot behavior of the one or more events; and
apply a validation logic to the one or more events and the machine learning model,
wherein the validation logic comprises performing an error analysis, and
wherein during the error analysis, the at least one memory and the computer program code are further configured, with the at least one processor, to cause the apparatus at least to examine metrics comprising false positive rate, area under a receiver operating curve, area under a precision recall curve partitioned by browser environments, and internet service providers.
2. A method, comprising:
collecting time-series data on one or more events occurring on a webpage;
deriving classifications of the one or more events;
performing functional transformations of the time-series data;
determining potential features of the one or more events based on a combination of the classifications of the one or more events, and results of the functional transformation, wherein the classifications of the one or more events comprise bot traffic and human traffic, and wherein the results of the functional transformation comprise numeric data or categorical data;
training a machine learning model with the potential features;
determining, via the machine learning model, bot behavior and non-bot behavior of the one or more events; and
applying a validation logic to the one or more events and the machine learning model,
wherein the validation logic comprises performing an error analysis, and
wherein during the error analysis, the method further comprises examining metrics comprising false positive rate, area under a receiver operating curve, area under a precision recall.
3. A computer program, embodied on a non-transitory computer readable medium, the computer program comprising computer executable code, which, when executed by a processor, causes the processor to:
collect time-series data on one or more events occurring on a webpage;
derive classifications of the one or more events;
perform functional transformations of the time-series data;
determine potential features of the one or more events based on a combination of the classifications of the one or more events, and results of the functional transformation;
train a machine learning model with the potential features, wherein the classifications of the one or more events comprise bot traffic and human traffic, and wherein the results of the functional transformation comprise numeric data or categorical data;
determine, via the machine learning model, bot behavior and non-bot behavior of the one or more events; and
apply a validation logic to the one or more events and the machine learning model,
wherein the validation logic comprises performing an error analysis, and
wherein during the error analysis, the processor is further caused to examine metrics comprising false positive rate, area under a receiver operating curve, area under a precision recall.