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MobileTrust: Secure Knowledge Integration in VANETs
Vehicular Ad hoc NETworks (VANET) are becoming popular due to the emergence of the Internet of Things and ambient intelligence applications. In such networks, secure resource sharing functionality is accomplished by incorporating trust schemes. Current solutions adopt peer-to-peer technologies that can cover the large operational area. However, these systems fail to capture some inherent properties of VANETs, such as fast and ephemeral interaction, making robust trust evaluation of crowdsourcing challenging. In this article, we propose MobileTrust—a hybrid trust-based system for secure resource sharing in VANETs. The proposal is a breakthrough in centralized trust computing that utilizes cloud and upcoming 5G technologies to provide robust trust establishment with global scalability. The ad hoc communication is energy-efficient and protects the system against threats that are not countered by the current settings. To evaluate its performance and effectiveness, MobileTrust is modelled in the SUMO simulator and tested on the traffic features of the small-size German city of Eichstatt. Similar schemes are implemented in the same platform to provide a fair comparison. Moreover, MobileTrust is deployed on a typical embedded system platform and applied on a real smart car installation for monitoring traffic and road-state parameters of an urban application. The proposed system is developed under the EU-founded THREAT-ARREST project, to provide security, privacy, and trust in an intelligent and energy-aware transportation scenario, bringing closer the vision of sustainable circular economy
Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter
Microblogs are increasingly exploited for predicting prices and traded
volumes of stocks in financial markets. However, it has been demonstrated that
much of the content shared in microblogging platforms is created and publicized
by bots and spammers. Yet, the presence (or lack thereof) and the impact of
fake stock microblogs has never systematically been investigated before. Here,
we study 9M tweets related to stocks of the 5 main financial markets in the US.
By comparing tweets with financial data from Google Finance, we highlight
important characteristics of Twitter stock microblogs. More importantly, we
uncover a malicious practice - referred to as cashtag piggybacking -
perpetrated by coordinated groups of bots and likely aimed at promoting
low-value stocks by exploiting the popularity of high-value ones. Among the
findings of our study is that as much as 71% of the authors of suspicious
financial tweets are classified as bots by a state-of-the-art spambot detection
algorithm. Furthermore, 37% of them were suspended by Twitter a few months
after our investigation. Our results call for the adoption of spam and bot
detection techniques in all studies and applications that exploit
user-generated content for predicting the stock market
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