Blockchain technology has become almost as famous for incidents involving security breaches as for its innovative potential. We shed light on the prevalence and nature of these incidents through a database structured using the STIX format. Apart from OPSEC-related incidents, we find that the nature of many incidents is specific to blockchain technology. Two categories stand out: smart contracts, and techno-economic protocol incentives. For smart contracts, we propose to use recent advances in software testing to find flaws before deployment. For protocols, we propose the PRESTO framework that allows us to compare different protocols within a five-dimensional framework.

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Tor is a popular 'darknet', a network that aims to conceal its users' identities and online activities. Darknets are composed of host machines that cannot be accessed by conventional means, which is why the content they host is typically not indexed by traditional search engines like Google and Bing. On Tor, web content and other types of services can anonymously be made available as so-called hidden services. Obviously, where anonymity can be a vehicle for whistleblowers and political dissidents to exchange information, the reverse of the medal is that it also attracts malicious actors. In our research, we aim to develop a detailed understanding of what Tor is being used for. We applied classification and topic model-based text mining techniques to the content of over a thousand Tor hidden services in order to model their thematic organization and linguistic diversity. As far as we are aware, this paper presents the most comprehensive content-based analysis of Tor to date.

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Motivation-Supporting knowledge workers in their self-management by providing them overviews of performed tasks. Research approach-Computer interaction data of knowledge workers was logged during their work. For each user different classifiers were trained and compared on their performance on recognizing 12 specified tasks. Findings/Design-After only a few hours of training data reasonable classification accuracy can be achieved. There was not one classifier that suited all users best. Take away message-Task recognition based on knowledge workers' computer activities is feasible with little training, although personalization is an important issue.