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WhatsApp is a popular messaging app used by over a billion users around the globe. Due to this popularity, spam on WhatsApp is an important issue. Despite this, the distribution of spam via WhatsApp remains understudied by researchers, in part because of the end-to-end encryption offered by the platform. This paper addresses this gap by studying spam on a dataset of 2.6 million messages sent to 5,051 public WhatsApp groups in India over 300 days. First, we characterise spam content shared within public groups and find that nearly 1 in 10 messages is spam. We observe a wide selection of topics ranging from job ads to adult content, and find that spammers post both URLs and phone numbers to promote material. Second, we inspect the nature of spammers themselves. We find that spam is often disseminated by groups of phone numbers, and that spam messages are generally shared for longer duration than non-spam messages. Finally, we devise content and activity based detection algorithms that can counter spam.
In this paper, we present an end-to-end view of IoT security and privacy and a case study. Our contribution is three-fold. First, we present our end-to-end view of an IoT system and this view can guide risk assessment and design of an IoT system. We
As the Internet of Things (IoT) emerges over the next decade, developing secure communication for IoT devices is of paramount importance. Achieving end-to-end encryption for large-scale IoT systems, like smart buildings or smart cities, is challengin
Complex environments and tasks pose a difficult problem for holistic end-to-end learning approaches. Decomposition of an environment into interacting controllable and non-controllable objects allows supervised learning for non-controllable objects an
The production of counterfeit money has a long history. It refers to the creation of imitation currency that is produced without the legal sanction of government. With the growth of the cryptocurrency ecosystem, there is expanding evidence that count
Decomposable tasks are complex and comprise of a hierarchy of sub-tasks. Spoken intent prediction, for example, combines automatic speech recognition and natural language understanding. Existing benchmarks, however, typically hold out examples for on