Todays WWW consists of more than just information. The WWW provides a large number of services, which often require identification of it's users. This has lead to the fact that today users have to maintain a large number of different credentials for different websitesdistributed or shared identification system are not widely deployed. Furthermore current authorisation systems requires strict centralisation of the authorisation procedure-users themselves are usually not enabled to authorise their trusted friends to access services, although often this would be beneficial for services and businesses on the Web. In this article we present D-FOAF, a distributed identity management system which deploys social networks. We show how information inherent in social networks can be utilised to provide community driven access rights delegation and we analyse algorithms for managing distributed identity, authorisation and access rights checking. Finally we show how the social networking information can be protected in a distributed environment.

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Modern malware typically makes use of a domain generation algorithm (DGA) to avoid command and control domains or IPs being seized or sinkholed. This means that an infected system may attempt to access many domains in an attempt to contact the command and control server. Therefore, the automatic detection of DGA domains is an important task, both for the sake of blocking malicious domains and identifying compromised hosts. However, many DGAs use English wordlists to generate plausibly clean-looking domain names; this makes automatic detection difficult. In this work, we devise a notion of difficulty for DGA families called the smashword score; this measures how much a DGA family looks like English words. We find that this measure accurately reflects how much a DGA family's domains look like they are made from natural English words. We then describe our new modeling approach, which is a combination of a novel recurrent neural network architecture with domain registration side information. Our experiments show the model is capable of effectively identifying domains generated by difficult DGA families. Our experiments also show that our model outperforms existing approaches, and is able to reliably detect difficult DGA families such as matsnu, suppobox, rovnix, and others. The model's performance compared to the state of the art is best for DGA families that resemble English words. We believe that this model could either be used in a standalone DGA domain detector-such as an endpoint security application-or alternately the model could be used as a part of a larger malware detection system. CCS CONCEPTS• Security and privacy → Malware and its mitigation; Artificial immune systems; Web protocol security; • Computing methodologies → Neural networks.

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