Biblio

With more than a trillion web pages, there is a plethora of content available for consumption. Search Engine queries invariably lead to overwhelming information, parts of it relevant and some others irrelevant. Often the information provided can be conflicting, ambiguous, and inconsistent contributing to the loss of credibility of the content. In the past, researchers have proposed approaches for credibility assessment and enumerated factors influencing the credibility of web pages. In this work, we detailed a WEBCred framework for automated genre-aware credibility assessment of web pages. We developed a tool based on the proposed framework to extract web page features instances and identify genre a web page belongs to while assessing it's Genre Credibility Score ( GCS). We validated our approach on `Information Security' dataset of 8,550 URLs with 171 features across 7 genres. The supervised learning algorithm, Gradient Boosted Decision Tree classified genres with 88.75% testing accuracy over 10 fold cross-validation, an improvement over the current benchmark. We also examined our approach on `Health' domain web pages and had comparable results. The calculated GCS correlated 69% with crowdsourced Web Of Trust ( WOT) score and 13% with algorithm based Alexa ranking across 5 Information security groups. This variance in correlation states that our GCS approach aligns with human way ( WOT) as compared to algorithmic way (Alexa) of web assessment in both the experiments.

What is the role of deterrence in an age where adept hackers can credibly hold strategic assets at risk? Do conventional frameworks of deterrence maintain their applicability and meaning against state actors in cyberspace? Is it possible to demonstrate credibility with either in-domain or cross-domain signaling or is cyberspace fundamentally ill-suited to the application of deterrence frameworks? Building on concepts from both rational deterrence theory and cognitive theories of deterrence this work attempts to leverage relevant examples from both within and beyond cyberspace to examine applicability of deterrence in the digital age and for digital tools in an effort to shift the conversation from Atoms to Bits and Bytes.

This paper presents a framework for multiagent systems trust modeling that reasons about both user credibility and user similarity. Through simulation, we are able to show that our approach works well in social networking environments by presenting messages to users with high predicted benefit.

This paper presents a credibility model to assess trust of Web services. The model relies on consumers' ratings whose accuracy can be questioned due to different biases. A category of consumers known as strict are usually excluded from the process of reaching a majority consensus. We demonstrated that this exclusion should not be. The proposed model reduces the gap between these consumers' ratings and the current majority rating. Fuzzy clustering is used to compute consumers' credibility. To validate this model a set of experiments are carried out.