Website Fingerprinting Attacks Based on Homology Analysis

Abstract

Website fingerprinting attacks allow attackers to determine the websites that users are linked to, by examining the encrypted traffic between the users and the anonymous network portals. Recent research demonstrated the feasibility of website fingerprinting attacks on Tor anonymous networks with only a few samples. Thus, this paper proposes a novel small-sample website fingerprinting attack method for SSH and Shadowsocks single-agent anonymity network systems, which focuses on analyzing homology relationships between website fingerprinting. Based on the latter, we design a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) attack classification model that achieves 94.8% and 98.1% accuracy in classifying SSH and Shadowsocks anonymous encrypted traffic, respectively, when only 20 samples per site are available. We also highlight that the CNN-BiLSTM model has significantly better migration capabilities than traditional methods, achieving over 90% accuracy when applied on a new set of monitored sites with only five samples per site. Overall, our experiments demonstrate that CNN-BiLSTM is an efficient, flexible, and robust model for website fingerprinting attack classification.

A Survey on Deep Learning for Website Fingerprinting Attacks and Defenses

“…When the size of the world was significantly increased, its performance degraded significantly to 30% precision and 70% recall. [26] SDAE, CNN, LSTM DF [27] CNN GRU and ResNet [28] GRU, ResNet-50 p-FP [29] MLP, CNN Cache-based WF [30], [31] CNN, LSTM Var-CNN [32] ResNet-18 Tik-Tok [34] DF 2ch-TCN [35] CNN Realistic WF [109] CNN, LSTM Multi-session WF [39] LSTM Side-channel informationbased WF [41] CNN, LSTM BurNet [43] CNN DNNF [44] CNN GAP-WF [45] GNN Cross-trace WF [46] DF DNN with Blind adversarial training [2] DF DNN with Tripod data augmentation [47] DF, Var-CNN, ResNet-18, ResNet-34, VGG-16, VGG-19 DNN with HDA data augmentation [48] Var-CNN, ResNet-34 Microarchitecture-based WF [49] 1-D CNN BAPM [110] CNN, Self-attention FDF [52] CNN, FC, Self-attention snWF [53] CNN WFD [111] 1-D ResNets DNN with Minipatch adversarial training [54] DF DNN with Bionic data augmentation [55] Var-CNN Semi-supervised learning GANDaLF [40] GAN PAS [114] DCNN, DF, AWF Transfer learning AF [42] Domain adversarial network TLFA [51] CNN, MLP Metric-learning TF [33] Triplet networks CPWF [38] CNN CNN-BiLSTM-based Siamese networks [50] Siamese networks, CNN, LSTM Online WF [56] TF Meta-learning MBL [57] CNN…”

“…[50] studied and proposed a homology analysis-based fewshot WF attack, relying on a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model. Chen et al [51] studied few-shot website fingerprinting attack where only a few training samples per website were available, introduced a novel Transfer Learning Fingerprinting Attack (TLFA) that can transfer knowledge from the labeled training data of websites disjoint and independent to the target websites, TLFA employed embedding CNN model in the pre-training stage, and explored multivariate logistic regression (LR), support vector machine (SVM) with linear kernel, multilayer perceptron (MLP) in the fine-tune stage.…”