The Internet's combination of low communication cost, global reach, and functional anonymity has allowed fraudulent scam volumes to reach new heights. Designing effective interventions requires first understanding the context: how scammers reach potential victims, the earnings they make, and any potential bottlenecks for durable interventions. In this short paper, we focus on these questions in the context of cryptocurrency giveaway scams, where victims are tricked into irreversibly transferring funds to scammers under the pretense of even greater returns. Combining data from Twitter (also known as X), YouTube and Twitch livestreams, landing pages, and cryptocurrency blockchains, we measure how giveaway scams operate at scale. We find that 1 in 1000 scam tweets, and 4 in 100,000 livestream views, net a victim, and that scammers managed to extract nearly $4.62 million from just hundreds of victims during our measurement window.

Nowadays, there is an increasing demand for wireless and mobile networks that guarantee secure and reliable data communication among users. To enable data exchanges, authentication mechanisms need to be in place to dynamically verify the identity of communicating parties. Authentication is used as a base to deploy security mechanisms and exchange information. However, authentication is static and it does not provide any information regarding the trustworthiness of communicating peers. Trust is a complex issue as it lies in the complexity of human behavior. Trust has typically been evaluated by measuring individual performance on a specific functionality or generally measuring specific behavior and translating it to a trust value. In this paper we define properties and requirements for trust at the individual and system level. Contrary to previous work, we consider trust in a non-linear manner that matches behavior in social networks. In addition, trust evaluations do not only take into account individual behavior independently, but also focus on its impact on the system. Finally, we present a novel 3-d trust model that is structured to reflect the overall trustworthiness of a system and responds to changes in node behavior. It provides a simplified and concurrently dynamic way of aggregating and representing trust and can be used to guide network functionalities, such as routing, or more generally evaluate trust in collaborative modes of interaction among individuals. An example application is also provided.