Abstract The HTTPS certificate ecosystem has long been a key topic in cybersecurity, yet the certificate landscape of Android applications remains insufficiently studied. In particular, while China has actively promoted the adoption of China’s national cryptographic algorithms in recent years, their actual deployment within the Chinese Android certificate ecosystem remains unclear. In this study, we analyzed TLS traffic from 19,980 applications in the Huawei App Market and extracted 131,933 certificate chains. While most certificates are properly configured, we identified 530 certificates with security risks, affecting 2043 applications. Notably, three SDK-related risk certificates were propagated across 1462 applications, substantially widening their security impact. Only 94 certificates using China’s national cryptographic algorithms were found, all within 89 financial applications, indicating deployment driven mainly by regulatory compliance. Furthermore, nearly 99% of leaf certificates chain back to foreign root Certificate Authorities, underscoring a strong dependency that may pose digital sovereignty risks under geopolitical uncertainty. This study highlights the existing challenges in the Chinese Android certificate ecosystem, particularly in terms of security and digital sovereignty, and offers relevant recommendations for improvement.

Abstract With Internet access shifting from desktop-driven to mobile-driven, application-level mobile traffic identification has become a research hotspot. Although considerable progress has been made in this research field, two obstacles are hindering its further development. Firstly, there is a lack of sharable labeled mobile traffic datasets. Although it is easy to capture mobile traffic, labeling traffic at the application level is non-trivial. Besides, researchers usually hold a conservative attitude toward publishing their datasets for privacy concerns. Secondly, most of the datasets used by existing studies are inadequate to evaluate the proposed methods, since they usually have the problems of inaccurate labels, small scale and simple collection configurations. To tackle these two obstacles, a mobile traffic collection is carried out in this paper. The collected traffic has the advantages of large-scale data size, accurate application-level labels and diverse collection configurations. Then, the collected traffic is anonymized carefully to make it public. Several mobile traffic identification methods are compared based on our anonymized dataset, which proves the applicability of our dataset.

Authentication and data protection (both integrity and confidentiality) between the network and cellular devices are two fundamental security features in LTE and IMS networks. The first is implemented via authentication and key agreement mechanisms and can be compromised by relaying authentication parameters. The second security feature builds on the first one and is activated through corresponding security setup procedures. This work intends to investigate whether these basic security procedures are securely implemented and deployed in commercial networks. We analyzed the de facto situation of these security features in three major operators in China and found several new and previously disclosed configuration and implementation flaws that do not conform to specifications. These vulnerabilities allow attackers to disable LTE and IMS data protection mechanisms. We further propose novel proof-of-concept attacks to exploit the identified vulnerabilities includingIMEIandPhone Number Catching,SMSandCall ImpersonationandInterceptionattacks. To show the urgency of addressing these security issues and thus secure the real-world telecom networks, we successfully demonstrated these attacks in practice using open-source SDR tools as they have serious implications. For instance, the interception attacks undermine the widely-used SMS verification code security mechanism. We also discuss countermeasures to resist the proposed attacks.

Traffic classification has attracted public attention for a long time because of its essential role in network management. However, the presence of zero-day traffic, network traffic generated by previously unknown applications, leads to a significant reduction in the practicability and effectiveness of conventional traffic classification schemes. This poster innovatively proposes a traffic classification scheme named RLCS to accomplish the high accurate traffic classification task in hybrid zero-day traffic. The evaluations with real-world traffic verify the effectiveness and broad applicability of RLCS.