Argus: Traffic Behavior Based Prediction of Internet User Demographics through Hierarchical Neural Network

Abstract

Predicting internet user demographics based on traffic behavior analysis can provide effective clues for the decision making of network administrators. Nonetheless, most of the existing researches overly rely on hand-crafted features, and they also suffer from the shallowness of information mining and the limitation in prediction targets. This paper proposes Argus, a hierarchical neural network solution to the prediction of Internet user demographics through traffic analysis. Argus is a hierarchical neural-network structure composed of an autoencoder for embedding and a fully-connected net for prediction. In the embedding layer, the high-level features of the input data are learned, with a customized regularization method to enforce their discriminative power. In the classification layer, the embeddings are converted into the label predictions of the sample. An integrated loss function is provided to Argus for end-to-end learning and architecture control. Argus has exhibited promising performances in experiments based on real-world dataset, where most of the metrics outperform those achieved by common machine learning techniques on multiple prediction targets. Further experiments reveal that the integrated loss function is capable of promoting Argus performance, and the contribution of a specific loss component during the training process is validated. Empirical settings for hyper parameters are given according to the experiments.