Recent advancements in deep learning (DL) have introduced new security challenges in the form of side channel attacks. A prime example is the Website Fingerprinting Attack (WFA), which targets anonymity networks like Tor, enabling attackers to unveil users’ protected browsing activities from traffic data. While state-of-the-art WFAs have achieved remarkable results, they often rely on unrealistic single-website assumptions. In this paper, we undertake an exhaustive exploration of Multi-Tab Website Fingerprinting Attacks (MTWFAs) in more realistic scenarios. We delve into MTWFAs and introduce MTWFA-SEG, a task involving the fine-grained packet-level classification within multi-tab Tor traffic. By employing deep learning models, we reveal their potential to threaten user privacy by discerning visited websites and browsing session timing. We design an improved fully convolutional model for MTWFA-SEG, which are enhanced by both network architecture advances and traffic data instincts.
In the evaluations on interlocking browsing datasets, the proposed models achieve remarkable accuracy rates of over 68.6%, 71.8%, and 76.1% in closed, imbalanced open, and balanced open-world settings, respectively. Furthermore, the proposed models exhibit substantial robustness across diverse train-test settings. We further validate our designs in a coarse-grained task, MTWFA-MultiLabel, where they not only achieve state-of-the-art performance but also demonstrate high robustness in challenging situations.

To overcome the limitations of long latency and privacy concerns from cloud computing, edge computing along with distributed machine learning such as federated learning (FL), has gained much attention and popularity in academia and industry. Most existing work on FL over the edge mainly focuses on optimizing the training of one shared global model in edge systems. However, with the increasing applications of FL in edge systems, there could be multiple FL models from different applications concurrently being trained in the shared edge cloud. Such concurrent training of these FL models can lead to edge resource competition (for both computing and network resources), and further affect the FL training performance of each other. Therefore, in this paper, considering a multi-model FL scenario, we formulate a joint participant selection and learning optimization problem in a shared edge cloud. This joint optimization aims to determine FL participants and the learning schedule for each FL model such that the total training cost of all FL models in the edge cloud is minimized. We propose a multi-stage optimization framework by decoupling the original problem into two or three subproblems that can be solved respectively and iteratively. Extensive evaluation has been conducted with real-world FL datasets and models. The results have shown that our proposed algorithms can reduce the total cost efficiently compared with prior algorithms.