Biblio

The collaborative recommendation mechanism is beneficial for the subject in an open network to find efficiently enough referrers who directly interacted with the object and obtain their trust data. The uncertainty analysis to the collected trust data selects the reliable trust data of trustworthy referrers, and then calculates the statistical trust value on certain reliability for any object. After that the subject can judge its trustworthiness and further make a decision about interaction based on the given threshold. The feasibility of this method is verified by three experiments which are designed to validate the model's ability to fight against malicious service, the exaggeration and slander attack. The interactive success rate is significantly improved by using the new model, and the malicious entities are distinguished more effectively than the comparative model.

Attack graphs used in network security analysis are analyzed to determine sequences of exploits that lead to successful acquisition of privileges or data at critical assets. An attack graph edge corresponds to a vulnerability, tacitly assuming a connection exists and tacitly assuming the vulnerability is known to exist. In this paper we explore use of uncertain graphs to extend the paradigm to include lack of certainty in connection and/or existence of a vulnerability. We extend the standard notion of uncertain graph (where the existence of each edge is probabilistically independent) however, as signicant correlations on edge existence probabilities exist in practice, owing to common underlying causes for dis-connectivity and/or presence of vulnerabilities. Our extension describes each edge probability as a Boolean expression of independent indicator random variables. This paper (i) shows that this formalism is maximally descriptive in the sense that it can describe any joint probability distribution function of edge existence, (ii) shows that when these Boolean expressions are monotone then we can easily perform uncertainty analysis of edge probabilities, and (iii) uses these results to model a partial attack graph of the Stuxnet worm and a small enterprise network and to answer important security-related questions in a probabilistic manner.