Xigao Li (Stony Brook University), Anurag Yepuri (Stony Brook University), Nick Nikiforakis (Stony Brook University)

As cryptocurrencies increase in popularity and users obtain and manage their own assets, attackers are pivoting from just abusing cryptocurrencies as a payment mechanism, to stealing crypto assets from end users. In this paper, we report on the first large-scale analysis of cryptocurrency giveaway scams. Giveaway scams are deceptively simple scams where attackers set up webpages advertising fake events and promising users to double or triple the funds that they send to a specific wallet address. To understand the population of these scams in the wild we design and implement CryptoScamTracker, a tool that uses Certificate Transparency logs to identify likely giveaway scams. Through a 6-month-long experiment, CryptoScamTracker identified a total of 10,079 giveaway scam websites targeting users of all popular cryptocurrencies. Next to analyzing the hosting and domain preferences of giveaway scammers, we perform the first quantitative analysis of stolen funds using the public blockchains of the abused cryptocurrencies, extracting the transactions corresponding to 2,266 wallets belonging to scammers. We find that just for the scams discovered in our reporting period, attackers have stolen the equivalent of tens of millions of dollars, organizing large-scale campaigns across different cryptocurrencies. Lastly, we find evidence that attackers try to re-victimize users by offering fund-recovery services and that some victims send funds multiple times to the same scammers.

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Tactics, Threats & Targets: Modeling Disinformation and its Mitigation

Shujaat Mirza (New York University), Labeeba Begum (New York University Abu Dhabi), Liang Niu (New York University), Sarah Pardo (New York University Abu Dhabi), Azza Abouzied (New York University Abu Dhabi), Paolo Papotti (EURECOM), Christina Pöpper (New York University Abu Dhabi)

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SoundLock: A Novel User Authentication Scheme for VR Devices...

Huadi Zhu (The University of Texas at Arlington), Mingyan Xiao (The University of Texas at Arlington), Demoria Sherman (The University of Texas at Arlington), Ming Li (The University of Texas at Arlington)

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OptRand: Optimistically Responsive Reconfigurable Distributed Randomness

Adithya Bhat (Purdue University), Nibesh Shrestha (Rochester Institute of Technology), Aniket Kate (Purdue University), Kartik Nayak (Duke University)

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