基于潜在特征表示学习的网络审查检测
Detecting Network-based Internet Censorship via Latent Feature Representation Learning
摘要：
互联网审查是一种具有社会重要性的现象，吸引了多学科的调查。一些研究小组，如Censored Planet，已经部署了大规模的互联网测量平台来收集网络可达性数据。然而，现有的研究通常依赖于手动设计的规则（即，使用审查指纹）来从数据中检测基于网络的互联网审查。虽然这种基于规则的方法产生了很高的真阳性检测率，但它面临着几个挑战：它需要人类的专业知识，很费力，并且无法检测到规则未捕获的任何审查。为了克服这些挑战，我们设计并评估了一个基于潜在特征表示学习的分类模型和一个基于图像的分类模型来检测基于网络的互联网审查。为了从网络可达性数据中推断潜在特征表示，我们提出了一个序列到序列的自动编码器来捕获数据中数据元素的结构和顺序。为了从推断的潜在特征估计审查事件的概率，我们依赖于密集连接的多层神经网络模型。我们的基于图像的分类模型将网络可达性数据记录编码为灰度图像，并使用密集的卷积神经网络将图像分类为审查或未审查。我们比较和评估这两种方法使用数据集从审查星球通过一个坚持的评价。这两种分类模型都能够检测基于网络的互联网审查，因为我们能够识别已知指纹未检测到的审查实例。潜在特征表示可能在数据中编码更多的细微差别，因为潜在特征学习方法发现了更多数量和更多样化的新审查实例。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
