Deep Learning for Encrypted Traffic Classification and Unknown Data Detection release_6kkbrzqu4neyvjfbul6o5tgsle

Abstract

Despite the widespread use of encryption techniques to provide confidentiality over Internet communications, mobile device users are still susceptible to privacy and security risks. In this paper, a novel Deep Neural Network (DNN) based on a user activity detection framework is proposed to identify fine-grained user activities performed on mobile applications (known as in-app activities) from a sniffed encrypted Internet traffic stream. One of the challenges is that there are countless applications, and it is practically impossible to collect and train a DNN model using all possible data from them. Therefore, in this work, we exploit the probability distribution of a DNN output layer to filter the data from applications that are not considered during the model training (i.e., unknown data). The proposed framework uses a time window-based approach to divide the traffic flow of activity into segments so that in-app activities can be identified just by observing only a fraction of the activity-related traffic. Our tests have shown that the DNN-based framework has demonstrated an accuracy of 90% or above in identifying previously trained in-app activities and an average accuracy of 79% in identifying previously untrained in-app activity traffic as unknown data when this framework is employed.
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