Biblio

The acceleration of extending popularity of Online Social Networks (OSNs) thanks to various services with which they provide people, is inevitable. This is why in OSNs security as a way to protect private data of users to be abused by unauthoritative people has a vital role to play. Trust evaluation is the security approach that has been utilized since the advent of OSNs. Graph-based approaches are among the most popular methods for trust evaluation. However, graph-based models need to employ limitations in the search process of finding trusted paths. This contributes to a reduction in trust accuracy. In this investigation, a learning-based model which with no limitation is able to find reliable users of any target user, is proposed. Experimental results depict 12% improvement in trust accuracy compares to models based on the graph-based approach.

This paper studies the synthesis of controllers for cyber-physical systems (CPSs) that are required to carry out complex tasks that are time-sensitive, in the presence of an adversary. The task is specified as a formula in metric interval temporal logic (MITL). The adversary is assumed to have the ability to tamper with the control input to the CPS and also manipulate timing information perceived by the CPS. In order to model the interaction between the CPS and the adversary, and also the effect of these two classes of attacks, we define an entity called a durational stochastic game (DSG). DSGs probabilistically capture transitions between states in the environment, and also the time taken for these transitions. With the policy of the defender represented as a finite state controller (FSC), we present a value-iteration based algorithm that computes an FSC that maximizes the probability of satisfying the MITL specification under the two classes of attacks. A numerical case-study on a signalized traffic network is presented to illustrate our results.