Despite the ability of the anonymous communication system The Onion Router(Tor)to obscure the content of communications,prior studies have shown that passive adversaries can still infer the websites visited by users t...Despite the ability of the anonymous communication system The Onion Router(Tor)to obscure the content of communications,prior studies have shown that passive adversaries can still infer the websites visited by users throughwebsite fingerprinting(WF)attacks.ConventionalWFmethodologies demonstrate optimal performance in scenarios involving single-tab browsing.Conventional WF methods achieve optimal performance primarily in scenarios involving single-tab browsing.However,in real-world network environments,users often engage in multitab browsing,which generates overlapping traffic patterns from different websites.This overlap has been shown to significantly degrade the performance of classifiers that rely on the single-tab assumption.To address this challenge,this paper proposes a Transformer-basedmulti-tab website fingerprinting(MT-WF)attack framework.Themodel employs an adaptive sliding windowmechanism to capture fine-grained features of traffic direction.Additionally,it incorporates a label-aware attention mechanism designed to dynamically separate and refine entangled traffic representations,enhancing the model’s ability to distinguish between overlapping traffic patterns.Furthermore,the model leverages global traffic patterns through multi-segment feature fusion and incorporates an incremental learning(IL)strategy to adapt to the continuously evolving website categories in open-world environments.Experimental results demonstrate that the proposedmethod achieves a top-2 precision of 0.78 in the closed-world setting.In the open-world scenario,the model attains an F1 score of 0.904,outperforming most existing baselines.The proposed method maintains superior performance even under challenging conditions,including WF defenses and concept drift.展开更多
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
文摘Despite the ability of the anonymous communication system The Onion Router(Tor)to obscure the content of communications,prior studies have shown that passive adversaries can still infer the websites visited by users throughwebsite fingerprinting(WF)attacks.ConventionalWFmethodologies demonstrate optimal performance in scenarios involving single-tab browsing.Conventional WF methods achieve optimal performance primarily in scenarios involving single-tab browsing.However,in real-world network environments,users often engage in multitab browsing,which generates overlapping traffic patterns from different websites.This overlap has been shown to significantly degrade the performance of classifiers that rely on the single-tab assumption.To address this challenge,this paper proposes a Transformer-basedmulti-tab website fingerprinting(MT-WF)attack framework.Themodel employs an adaptive sliding windowmechanism to capture fine-grained features of traffic direction.Additionally,it incorporates a label-aware attention mechanism designed to dynamically separate and refine entangled traffic representations,enhancing the model’s ability to distinguish between overlapping traffic patterns.Furthermore,the model leverages global traffic patterns through multi-segment feature fusion and incorporates an incremental learning(IL)strategy to adapt to the continuously evolving website categories in open-world environments.Experimental results demonstrate that the proposedmethod achieves a top-2 precision of 0.78 in the closed-world setting.In the open-world scenario,the model attains an F1 score of 0.904,outperforming most existing baselines.The proposed method maintains superior performance even under challenging conditions,including WF defenses and concept drift.
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.
