System and method for correlating historical attacks with diverse indicators to generate indicator profiles for detecting and predicting future network attacks

Abstract

An apparatus and method predict and detect network attacks by using a diverse set of indicators to measure aspects of the traffic and by encoding traffic characteristics using these indicators of potential attacks or anomalous behavior. The set of indicators is analyzed by supervised learning to automatically learn a decision rule which examines the temporal patterns in the coded values of the set of indicators to accurately detect and predict network attacks. The rules automatically evolve in response to new attacks as the system updates its rules periodically by analyzing new data and feedback signals about attacks associated with that data. To assist human operators, the system also provides human interpretable explanations of detection and prediction rules by pointing to indicators whose values contribute to a decision that there is an existing network attack or an imminent network attack. When such indictors are detected, an operator can take remediation actions.