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Online Social Networks (OSNs) enjoy high popularity, but their centralized architectures lead to intransparency and mistrust in the providers who can be the single point of failure. A solution is to adapt the OSN functionality to an underlying and fully distributed peer-to-peer (P2P) substrate. Several approaches in the field of OSNs based on P2P architectures have been proposed, but they share substantial P2P weaknesses and they suffer from low availability and privacy problems. In this work, we propose a distributed OSN which combines an underlying P2P architecture with friend-based data propagation and a Proof-of-Work (PoW) concept. ProofBook provides availability of user data, stability of the underlying network architecture and privacy improvements while it does not limit simple data sharing based on social relations.

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In this paper, we describe the design, the implementation and the evaluation of a dynamic honeypot architecture which can be offered as an additional security service for cloud users in a cloud that offers Infrastructure-as-a-Service (IaaS). Honeypots can protect original systems while revealing new and unknown attacks at the same time. The proposed dynamic honeypot architecture detects potential attacks in the initial phases and delays these attacks until a new honeypot virtual machine (VM) is extracted from the original VM which is under attack. The extraction process is a modifying VM live cloning process which leaves sensible data behind and prevents internal data loss. This way, the newly created honeypot VM runs the same software in exactly the same upto-date configuration. The honeypot controller redirects the delayed attack to the extracted honeypot VM and analyses its impact without risking the integrity of the original target VM. The proposed architecture benefits from the flexibility and adaptability of the cloud. It efficiently protects VMs of cloud users from contemporary network attacks while only few additional cloud resources are temporarily needed. The architecture deceives and misleads an attacker or an attacking source but does not influence the normal work-flow of the original VMs in the cloud. Based on a defined reporting format, cloud users can learn from attacks which have targeted their VMs and discover current misconfigurations and unknown vulnerabilities.

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State-of-the art trust and reputation systems seek to apply machine learning methods to overcome generalizability issues of experience-based Bayesian trust assessment. These approaches are, however, often model-centric instead of focussing on data and the complex adaptive system that is driven by reputation-based service selection. This entails the risk of unrealistic model assumptions. We outline the requirements for robust probabilistic trust assessment using supervised learning and apply a selection of estimators to a real-world data set, in order to show the effectiveness of supervised methods. Furthermore, we provide a representational mapping of estimator output to a belief logic representation for the modular integration of supervised methods with other trust assessment methodologies.