  Internet censorship is a phenomenon of societal importance and attracts
investigation from multiple disciplines. Several research groups, such as
Censored Planet, have deployed large scale Internet measurement platforms to
collect network reachability data. However, existing studies generally rely on
manually designed rules (i.e., using censorship fingerprints) to detect
network-based Internet censorship from the data. While this rule-based approach
yields a high true positive detection rate, it suffers from several challenges:
it requires human expertise, is laborious, and cannot detect any censorship not
captured by the rules. Seeking to overcome these challenges, we design and
evaluate a classification model based on latent feature representation learning
and an image-based classification model to detect network-based Internet
censorship.
  To infer latent feature representations fromnetwork reachability data, we
propose a sequence-to-sequence autoencoder to capture the structure and the
order of data elements in the data. To estimate the probability of censorship
events from the inferred latent features, we rely on a densely connected
multi-layer neural network model.
  Our image-based classification model encodes a network reachability data
record as a gray-scale image and classifies the image as censored or not using
a dense convolutional neural network. We compare and evaluate both approaches
using data sets from Censored Planet via a hold-out evaluation. Both
classification models are capable of detecting network-based Internet
censorship as we were able to identify instances of censorship not detected by
the known fingerprints. Latent feature representations likely encode more
nuances in the data since the latent feature learning approach discovers a
greater quantity, and a more diverse set, of new censorship instances.
