Aozhuo Sun (Institute of Information Engineering, Chinese Academy of Sciences), Jingqiang Lin (School of Cyber Science and Technology, University of Science and Technology of China), Wei Wang (Institute of Information Engineering, Chinese Academy of Sciences), Zeyan Liu (The University of Kansas), Bingyu Li (School of Cyber Science and Technology, Beihang University), Shushang Wen (School of Cyber Science and Technology, University of Science and Technology of China), Qiongxiao Wang (BeiJing Certificate Authority Co., Ltd.), Fengjun Li (The University of Kansas)

The certificate transparency (CT) framework has been deployed to improve the accountability of the TLS certificate ecosystem. However, the current implementation of CT does not enforce or guarantee the correct behavior of third-party monitors, which are essential components of the CT framework, and raises security and reliability concerns. For example, recent studies reported that 5 popular third-party CT monitors cannot always return the complete set of certificates inquired by users, which fundamentally impairs the protection that CT aims to offer. This work revisits the CT design and proposes an additional component of the CT framework, CT watchers. A watcher acts as an inspector of third-party CT monitors to detect any misbehavior by inspecting the certificate search services of a third-party monitor and detecting any inconsistent results returned by multiple monitors. It also semi-automatically analyzes potential causes of the inconsistency, e.g., a monitor’s misconfiguration, implementation flaws, etc. We implemented a prototype of the CT watcher and conducted a 52-day trial operation and several confirmation experiments involving 8.26M unique certificates of about 6,000 domains. From the results returned by 6 active third-party monitors in the wild, the prototype detected 14 potential design or implementation issues of these monitors, demonstrating its effectiveness in public inspections on third-party monitors and the potential to improve the overall reliability of CT.

View More Papers

ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning

Linkang Du (Zhejiang University), Min Chen (CISPA Helmholtz Center for Information Security), Mingyang Sun (Zhejiang University), Shouling Ji (Zhejiang University), Peng Cheng (Zhejiang University), Jiming Chen (Zhejiang University), Zhikun Zhang (CISPA Helmholtz Center for Information Security and Stanford University)

Read More

Why People Still Fall for Phishing Emails: An Empirical...

Asangi Jayatilaka (Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, School of Computing Technologies, RMIT University), Nalin Asanka Gamagedara Arachchilage (School of Computer Science, The University of Auckland), M. Ali Babar (Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide)

Read More

WIP: An Adaptive High Frequency Removal Attack to Bypass...

Yuki Hayakawa (Keio University), Takami Sato (University of California, Irvine), Ryo Suzuki, Kazuma Ikeda, Ozora Sako, Rokuto Nagata (Keio University), Qi Alfred Chen (University of California, Irvine), Kentaro Yoshioka (Keio University)

Read More