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.

View More Papers

Improving the Robustness of Transformer-based Large Language Models with...

Lujia Shen (Zhejiang University), Yuwen Pu (Zhejiang University), Shouling Ji (Zhejiang University), Changjiang Li (Penn State), Xuhong Zhang (Zhejiang University), Chunpeng Ge (Shandong University), Ting Wang (Penn State)

Read More

Work-in-Progress: Towards Browser-Based Consent Management

Gayatri Priyadarsini Kancherla and Abhishek Bichhawat (Indian Institute of Technology Gandhinagar)

Read More

Facilitating Threat Modeling by Leveraging Large Language Models

Isra Elsharef, Zhen Zeng (University of Wisconsin-Milwaukee), Zhongshu Gu (IBM Research)

Read More

Exploiting Diagnostic Protocol Vulnerabilities on Embedded Networks in Commercial...

Carson Green, Rik Chatterjee, Jeremy Daily (Colorado State University)

Read More