Combining multiple detection algorithms into a confidence score for bot detection

Abstract

A bot detection service associated with an overlay network operates to score traffic as a probability of being a bot, as opposed to returning a binary classification (i.e., bot or human). According to the approach herein, scoring is determined through probability estimates, wherein a score (the probability) is based on considering a set of detections concurrently. In one embodiment, all (or substantially all) triggered (current) threat detections contribute to the score. The preferred approach penalizes requests that fail all (or substantially all) combinations of detection algorithms. According to a further feature, an automated tuning (autotuning) is also applied, e.g., using real-time empirical statistical models, to adapt the measurement of false positive probability for one or more threat detection algorithms to suit customer traffic trends. The approach herein is also extensible to include any number of future threat detection algorithms.