CPWF: Cross-Platform Website Fingerprinting Based on Multi-Similarity Loss

Wang, Shihao; Wang, Liangmin; Yin, Shang-Nan; Hui, Zhao; Shentu, Hao

doi:10.1109/nana51271.2020.00020

Cited by 2 publications

(2 citation statements)

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“…Dahanayaka et al [36], [37] analyzed CNNs for WF attacks, and found that they focused mainly on transitions between uploads and downloads in trace fronts, exhibited few-shot learning capabilities, and outperformed RNNs due to their resilience to random shifts in data. Shihao Wang et al [38] presented a cross-platform website fingerprinting (CPWF) attack based on Multi-Similarity Loss which was introduced by deep metric learning to guide the deep learning model to extract effective feature sets. Ramezani et al [39] developed a multi-label classifier based on LSTM, that can predict the websites visited by a user in a certain period, the classifier utilized the server names appearing in chronological order in the TLSv1.2 and TLSv1.3 Client Hello packets as features.…”

Section: A Approachesmentioning

confidence: 99%

“…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…”

Section: Performancementioning

confidence: 99%

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A Survey on Deep Learning for Website Fingerprinting Attacks and Defenses

Liu

2023

IEEE Access

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The attacks and defenses on the information of which website pages are visited by users are important research subjects in the field of privacy enhancing technologies, they are termed as website fingerprinting (WF) attacks and defenses. Nowadays, deep learning is an important tool in many research areas, including WF attacks and defenses. In this paper, we offer a comprehensive survey on deep learning for WF attacks and defenses. After a brief introduction, we first summarize deep learning, WF attacks, and WF defenses. For deep learning, we review the common paradigms, architectures, and performance metrics. For WF attacks, we review the approaches, challenges and solutions. The approaches include deep learning, traditional machine learning, and other methods. Challenges and solutions cover multi-tab browsing, concept drift, and the base rate fallacy. For WF defenses, we review the strategies and approaches. Then, we survey deep learning for WF attacks, and deep learning for WF defenses. In deep learning for WF attacks, we survey in detail the deep learning paradigms, architectures of WF attack models, and the performance of several representative WF attack models, and look into the future. In deep learning for WF defenses, we survey the architecture, efficacy and overhead of deep learning models in WF defenses, and look into the future. In the end, we summarize this paper.

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Section: A Approachesmentioning

confidence: 99%