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

Stacking up the LLM Risks: Applied Machine Learning Security

Dr. Gary McGraw, Berryville Institute of Machine Learning

Read More

Eavesdropping on Controller Acoustic Emanation for Keystroke Inference Attack...

Shiqing Luo (George Mason University), Anh Nguyen (George Mason University), Hafsa Farooq (Georgia State University), Kun Sun (George Mason University), Zhisheng Yan (George Mason University)

Read More

WIP: Towards Practical LiDAR Spoofing Attack against Vehicles Driving...

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

Read More

TextGuard: Provable Defense against Backdoor Attacks on Text Classification

Hengzhi Pei (UIUC), Jinyuan Jia (UIUC, Penn State), Wenbo Guo (UC Berkeley, Purdue University), Bo Li (UIUC), Dawn Song (UC Berkeley)

Read More