Website Fingerprinting (WF) aims to deanonymize users on the Tor network by analyzing encrypted network traffic. Recent deep-learning-based attacks show high accuracy on undefended traces. However, they struggle against modern defenses that use tactics like injecting dummy packets and delaying real packets, which significantly degrade classification performance. Our analysis reveals that current attacks inadequately leverage the timing information inherent in traffic traces, which persists as a source of leakage even under robust defenses. Addressing this shortfall, we introduce a novel feature representation named the Inter-Arrival Time (IAT) histogram, which quantifies the frequencies of packet inter-arrival times across predetermined time slots. Complementing this feature, we propose a new CNN-based attack, WFCAT, enhanced with two architectural blocks designed to effectively extract and utilize timing information. The model employs convolutional kernels of varying sizes to capture multi-scale temporal features, which are then integrated through a weighted combination across feature channels. This channel-wise attention mechanism enables the model to adaptively emphasize informative patterns while suppressing noise, thereby improving its robustness against timing obfuscation. Our experiments validate that WFCAT substantially outperforms existing methods on defended traces in both closed- and open-world scenarios. Notably, WFCAT achieves over 59% accuracy against Surakav, a recently developed robust defense, marking an improvement of over 28% and 48% against the state-of-the-art attacks RF and Tik-Tok, respectively, in the closed-world scenario.

Website fingerprinting (WF) attacks, which covertly monitor user communications to identify the web pages they visit, pose a serious threat to user privacy. Existing WF defenses attempt to reduce attack accuracy by disrupting traffic patterns, but attackers can retrain their models to adapt, making these defenses ineffective. Meanwhile, their high overhead limits deployability. To overcome these limitations, we introduce a novel controllable website fingerprinting defense called TrapFlow based on backdoor learning. TrapFlow exploits the tendency of neural networks to memorize subtle patterns by injecting crafted trigger sequences into targeted website traffic, causing the attacker’s model to build incorrect associations during training. If the attacker attempts to adapt by training on such noisy data, TrapFlow ensures that the model internalizes the trigger as a dominant feature, leading to widespread misclassification across unrelated websites. Conversely, if the attacker ignores these patterns and trains only on clean data, the trigger behaves as an adversarial patch at inference time, causing model misclassification. To achieve this dual effect, we optimize the trigger using the Fast Levenshtein-like distance to maximize both its learnability and distinctiveness from normal traffic. Experiments show that TrapFlow significantly reduces the accuracy of the RF attack from 99% to 6% with 74% data overhead. This compares favorably against two SOTA defenses: FRONT reduces accuracy by only 2% at a similar overhead, while Palette achieves 32% accuracy, but with 48% more overhead. We further validate the practicality of our method in a real Tor network environment.

Smartphone users are susceptible to a privacy leakage attack called App Fingerprinting (AF), where traffic analysis is used to infer the apps in use. Despite packet encryption, AF attacks leverage packet size and timing information to identify apps, posing a privacy threat. However, existing attacks fail when a few apps are used concurrently, causing unsegmented traffic with app multiplexing and overlapping. The key reason is that they cannot accurately identify active time boundaries for the apps. This paper presents a novel AF attack, FOADA, the first to accurately predict both the location and label of a target app in traffic. FOADA approaches AF as an object detection problem, training a deep learning model to estimate boundary positions and classify traffic segments. Accurate boundary predictions help the model focus on the most relevant traffic segment, enhancing its classification performance. FOADA excels in handling noisy app traffic. With app multiplexing, it achieves an F1-score of 0.96 for predicting only app labels and an F1-score of 0.92 for predicting both app labels and their locations. FOADA surpasses the state-of-the-art attack PacketPrint, which achieves F1-scores of 0.80 and 0.48 in these two scenarios, respectively. The inference time of FOADA is 2,000 times faster than PacketPrint.

Tor, an onion-routing anonymity network, can be attacked by Website Fingerprinting (WF), which de-anonymizes encrypted web browsing traffic by analyzing its unique sequence characteristics. Although many defenses have been proposed, few have been implemented and tested in the real world; most state-of-the-art defenses were only simulated. Simulations fail to capture the real performance of these defenses as they make simplifying assumptions about the protocol stack and network conditions. To allow WF defenses to be analyzed as real implementations, we create WFDefProxy, the first general platform for WF defense implementation on Tor as pluggable transports. We implement three state-of-the-art WF defenses: FRONT, Tamaraw, and RegulaTor. We evaluate each defense extensively by directly collecting defended datasets under WFDefProxy. Our results show that simulation can be inaccurate in many cases. Specifically, Tamaraw’s time overhead was underestimated by 22% in one setting and overestimated by 24% in another. RegulaTor’s time overhead was underestimated by 30–40%. We find that a major source of simulation inaccuracy is that they cannot incorporate how packets depend on each other. We also find that adverse network conditions (which are ignored in simulation), especially congestion, can affect the evaluated overhead of defenses. These results show that it is important to evaluate defenses as implementations instead of only simulations to avoid errors in evaluation.

Website Fingerprinting (WF) attacks utilize size and timing information of encrypted network traffic to infer the user’s browsing activity, posing a great threat to privacy-enhancing technologies like Tor; nevertheless, Tor has not adopted any defense because existing defenses are not convincing enough to show their effectiveness. Some defenses have been overcome by newer attacks; other defenses are never implemented and tested in the real open-world scenario.In this paper, we propose Surakav, a tunable and practical defense that is effective against WF attacks with reasonable overhead. Surakav makes use of a Generative Adversarial Network (GAN) to generate realistic sending patterns and regulates buffered data according to the sampled patterns. We implement Surakav and evaluate it on the live Tor network. Experiments show that Surakav is able to reduce the attacker’s true positive rate by 57% with 55% data overhead and 16% time overhead, saving 42% data overhead compared to FRONT. In the heavyweight setting, Surakav outperforms the strongest known defense, Tamaraw, requiring 50% less overhead in data and time to lower the attacker’s true positive rate to only 8%. We also show that two existing defenses, Walkie-Talkie and TrafficSliver, can be fortified with our GAN-based trace generator.