In this article we address the challenge of detecting the generation and spreading of misleading information in the specific scenario of clickbait. Our contribution consists of a methodology that combines a deep neural network and an information divergence measure to overcome the limitations of deep learning techniques in this scenario. This analysis is conducted by considering a clickbait challenge dataset. We realise that the construction of the dataset used to study this kind of problems dramatically affects the performance of the model and, thus, its selection. Since clickbait is a result of the inconsistency between headlines and content, we integrate a divergence measure as a layer of a deep learning model. The resulting model overcomes the limitations of conventional machine learning and deep learning models in clickbait detection.

With the increase in connectivity, the popularization of cloud services, and the rise of the Internet of Things (IoT), decentralized approaches for trust management are gaining momentum. Since blockchain technologies provide a distributed ledger, they are receiving massive attention from the research community in different application fields. However, this technology does not provide with cybersecurity by itself. Thus, this survey aims to provide with a comprehensive review of techniques and elements that have been proposed to achieve cybersecurity in blockchain-based systems. The analysis is intended to target area researchers, cybersecurity specialists and blockchain developers. For this purpose, we analyze 272 papers from 2013 to 2020 and 128 industrial applications. We summarize the lessons learned and identify several matters to foster further research in this area.