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,e...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.展开更多
Saa S模式中租户与服务商之间一直存在信任问题,将服务商与租户分开管理,借助标签跨域访问则存在效率瓶颈和标签安全的问题。利用属性特征结合标签,设计出一个针对多租户跨域访问的方法,将标签根据属性分类管理,再把跨域访问的权限赋予...Saa S模式中租户与服务商之间一直存在信任问题,将服务商与租户分开管理,借助标签跨域访问则存在效率瓶颈和标签安全的问题。利用属性特征结合标签,设计出一个针对多租户跨域访问的方法,将标签根据属性分类管理,再把跨域访问的权限赋予各属性标签,实现平台内通过属性标签的跨域访问。最后,通过实验和分析验证了该属性标签保护方法的高效性。展开更多
基金supported partially by the National Natural Science Foundation of China(Nos.62172378,61572448,and 61827810)by the National Key Research and Development Program of China(No.2020YFB1707701).
文摘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.
