Ubiquitously deployed Internet of Things (IoT)- based automatic vehicle classification systems will catalyze data-driven traffic flow optimization in future smart cities and will transform the road infrastructure itself into a dynamically sensing Cyber-physical System (CPS). Although a wide range of different traffic sensing systems has been proposed, the existing solutions are not yet able to simultaneously satisfy the multitude of requirements, e.g., accuracy, robustness, cost-efficiency, and privacy preservation. In this paper, we present a novel approach, which exploits radio fingerprints - multidimensional attenuation patterns of wireless signals - for accurate and robust vehicle detection and classification. The proposed system can be deployed in a highly cost-efficient manner as it relies on off-the-shelf embedded devices which are installed into existing delineator posts. In a comprehensive field evaluation campaign, the performance of the radio fingerprinting-based approach is analyzed within an experimental live deployment on a German highway, where it is able to achieve a binary classification success ratio of more than 99% and an overall accuracy of 93.83% for a classification task with seven different classes.
MAppPrint: Robust and Real-time Mobile App Fingerprinting via Traffic Behavior Correlation Graphs
MOE KLINNS Lab, Xi’an Jiaotong University（西安交通大学教育部知识工程实验室）；The Hong Kong Polytechnic University（香港理工大学）；PolyU Research Centre for Privacy and Security Technologies in Future Smart Systems（香港理工大学未来智慧系统隐私与安全技术研究）
STAR: Semantic-Traffic Alignment and Retrieval for Zero-Shot HTTPS Website Fingerprinting
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络与信息安全学院）；Institute for Network Sciences and Cyberspace, Tsinghua University（清华大学网络科学与空间研究院）
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Guangzhou University（广州大学）
密码协议被广泛用于保护用户的隐私和避免暴露隐私信息。QUIC（Quick UDP Internet Connections），包括最初由Google设计的版本（GQUIC）和IETF标准化的版本（IQUIC），作为传统HTTP的替代品，展示了其独特的传输特性：基于UDP进行加密资源传输，加速网页渲染。然而，现有的基于TCP的加密传输方案容易受到网站指纹（WFP）攻击，允许攻击者通过窃听传输信道来推断用户访问的网站。GQUIC和IQUIC能否有效抵御此类攻击值得研究。本文从流量分析的角度研究了GQUIC、IQUIC和HTTPS对WFP攻击的脆弱性。大量实验表明，在早期流量场景下，GQUIC、IQUIC和HTTPS中GQUIC最容易受到WFP攻击，而IQUIC比HTTPS更容易受到攻击，但在正常全流量场景下，三种协议的脆弱性相似。特征迁移分析表明，在正常的全业务场景下，协议间的大部分特征是可以迁移的。然而，结合潜在特征表示的定性分析，我们发现，在早期流量时，传输是低效的，因为GQUIC，IQUIC和HTTPS在早期流量上的流量分布的变化幅度显着不同。通过将一次WFP攻击升级为多次WFP Top-a攻击，我们发现在GQUIC和IQUIC上的攻击准确率分别达到95.4%和95.5%，而在HTTPS上的攻击准确率仅为60.7%。我们还证明了IQUIC的脆弱性只是轻微地依赖于网络环境。
More Than Meets the Eye: A Semantics-Aware Traffic Augmentation Framework for Generalizable Website Fingerprinting
School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China（北京邮电大学信息安全学院）；School of Computer Science and Engineering, Beihang University, Beijing, China（北京航空航天大学计算机科学与工程学院）；Faculty of Computing, Harbin Institute of Technology, Harbin, China（哈尔滨工业大学计算机学院）；School of Computer Science and Engineering, Guangxi Normal University, Guilin, China（广西师范大学计算机科学与工程学院）；Zhongguancun Laboratory, Beijing, China（中关村实验室）
CipherSight: Robust Website Fingerprinting via Record-Resource Semantic Supervision under Distribution Shifts
Harbin Institute of Technology（哈尔滨工业大学）；Zhongguancun Laboratory（中关村实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
