Abstract: Machine learning techniques for classifying encrypted traffic with a high degree of accuracy. The techniques do not require decrypting any traffic and may not require any manually-labeled traffic samples. An automated system uses an application of interest to perform a large number of user actions of various types. The system further records, in a log, the respective times at which the actions were performed. The system further receives the encrypted traffic exchanged between the system and the application server, and records properties of this traffic in a time series. Subsequently, by correlating between the times in the log and the times at which the traffic was received, the system matches each of the user actions with a corresponding portion of the traffic, which is assumed to have been generated by the user action. The system thus automatically builds a labeled training set, which may be used to train a network-traffic classifier.

Abstract: Machine learning techniques for classifying encrypted traffic with a high degree of accuracy. The techniques do not require decrypting any traffic and may not require any manually-labeled traffic samples. An automated system uses an application of interest to perform a large number of user actions of various types. The system further records, in a log, the respective times at which the actions were performed. The system further receives the encrypted traffic exchanged between the system and the application server, and records properties of this traffic in a time series. Subsequently, by correlating between the times in the log and the times at which the traffic was received, the system matches each of the user actions with a corresponding portion of the traffic, which is assumed to have been generated by the user action. The system thus automatically builds a labeled training set, which may be used to train a network-traffic classifier.