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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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)

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Random Spoofing Attack against Scan Matching Algorithm SLAM (Long)

Masashi Fukunaga (MitsubishiElectric), Takeshi Sugawara (The University of Electro-Communications)

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

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

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