Yi Han, Shujiang Wu, Mengmeng Li, Zixi Wang, and Pengfei Sun (F5)

Online fraud has emerged as a formidable challenge in the digital age, presenting a serious threat to individuals and organizations worldwide. Characterized by its ever-evolving nature, this type of fraud capitalizes on the rapid development of Internet technologies and the increasing digitization of financial transactions. In this paper, we address the critical need to understand and combat online fraud by conducting an unprecedented analysis based on extensive real-world transaction data.

Our study involves a multi-angle, multi-platform examination of fraudsters' approaches, behaviors and intentions. The findings of our study are significant, offering detailed insights into the characteristics, patterns and methods of online fraudulent activities and providing a clear picture of the current landscape of digital deception. To the best of our knowledge, we are the first to conduct such large-scale measurements using industrial-level real-world online transaction data.

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Gorka Abad (Radboud University & Ikerlan Technology Research Centre), Oguzhan Ersoy (Radboud University), Stjepan Picek (Radboud University & Delft University of Technology), Aitor Urbieta (Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA))

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DynPRE: Protocol Reverse Engineering via Dynamic Inference

Zhengxiong Luo (Tsinghua University), Kai Liang (Central South University), Yanyang Zhao (Tsinghua University), Feifan Wu (Tsinghua University), Junze Yu (Tsinghua University), Heyuan Shi (Central South University), Yu Jiang (Tsinghua University)

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Sourav Das (University of Illinois at Urbana-Champaign), Zhuolun Xiang (Aptos), Ling Ren (University of Illinois at Urbana-Champaign)

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