Tor is one of the most widely adopted anonymity networks, yet its anonymity can be undermined by adversaries through flow correlation attacks. Current mainstream technologies focus on exploiting the sequence characteristics of packet lengths and timestamps to execute attacks. However, the padding mechanism of the Tor network and time delays caused by multi-hop relays obscure these single-modal features. Additionally, the diversity of network services and the randomness of user behavior result in sparse packet distributions, which impact model training and inference. In this paper, we propose SSRCorr, a novel self-supervised learning framework for flow correlation attacks, incorporating the Flow Feature Aggregation (FFA) module and Global-Local Fusion (GLoF) Encoder to address these challenges. Firstly, we construct a Byte-based Traffic Aggregation Matrix (BTAM) by integrating time and length sequences and applying two data augmentation methods tailored for Tor flow correlation, thereby reducing the impact of Tor network noise on attack effectiveness. Secondly, we employ GLoF to extract features from the output by FFA and fuse the global context information of the traffic, thus mitigating the impact of low-information traffic on model performance. Experiments show that SSRCorr achieves a TPR of 96%, surpassing other methods, and maintains robust performance under temporal drift and obfuscation, supporting future research on countering anonymity system defenses.

Website classification proves crucial for tasks like malicious website detection and information management. Current methods typically focus on effective feature extraction and algorithm selection to create balanced website datasets, often leading to decreased performance due to data imbalance. In this study, we propose an intelligent website classification method(WebPromptM2) based on prompt-based learning with multimodal features. We design a prompt template which incorporates the textual and visual elements of the website, thereby facilitating a multimodal representation of the website, then leverage domain-specific expertise to establish mapping relationships between website categories and a label word set. Finally, we fine-tune the masked pre-trained language model (PLM) and map the prediction results to the categories. We find that our method increases recognition accuracy of tail classes and achieves superior performance on long-tail and short-tail datasets.

Website fingerprinting (WF) attacks pose a threat to privacy of web activity, especially on anonymity networks such as Tor. Recent studies show that the deep neural network (DNN) significantly improves the impact of website fingerprinting attacks. Especially, DNN-based attack undermines the existing defense methods which are mainly rely on the manually designed rule. In this paper, we present a novel defense that generates universal perturbation that can transform original examples to adversarial examples which is effectively defending against a specific WF model. The proposed defense is evaluated on state-of-the-art DNN attack over a public Tor traffic dataset. The experimental results show our adversarial example generation method performs better than the baseline methods. The proposed defense defeats all existing WF attacks based on deep neural networks with a low overhead. Comparing with state-of-the-art defenses such as Walkie-Talkie and WTF-PAD with a lower bound of 31% and 64% overheads, the proposed defense achieves identical defense performance with at least 50% bandwidth overhead saving.