Trust has been reported as a key role in e-business, especially for a monetary based system like e-commerce. Therefore, many previous studies have been conducted to investigate the antecedents and consequences of consumer trust. But there has been little work done on establishing sensible solutions for leveraging consumer trust. Furthermore, previous studies in managerial trust have not demonstrated trust management that can illustrate a method to link their solutions with the consumers' point of view. In this paper, we therefore propose a practical roadmap for establishing trust management strategy that is consistent with consumer perceptions. Within this roadmap, firstly, a component extraction is performed on survey data in order to identify the quality criteria that actually impact buying decision process. Based on these criteria, the sensible factors for establishing trust and satisfaction are discovered from the regression analysis. Then, the prediction equations for trust and satisfaction are generated. After that the results of prediction equations are applied with a fuzzy linguistic approach in order to convert these results in linguistic terms. Finally, trust management strategy is established. By using our proposed method, website managers can identify which are the quality criteria that are consistent with their customer perceptions and they can use these criteria as a basis for improving trust and satisfaction in their websites.

The rise of the Internet accelerates the creation of various large-scale online social networks, which can be described the relationships and activities between human beings. The online social networks relationships in real world are too big to present with useful information to identify the criminal or cyber-attacks. This research proposed new information security analytic model for online social networks, which called Security Visualization Analytics (SVA) Model. SVA Model used the set of algorithms (1) Graph-based Structure algorithm to analyze the key factors of influencing nodes about density, centrality and the cohesive subgroup to identify the influencing nodes of anomaly and attack patterns (2) Supervised Learning with oneR classification algorithm was used to predict new links from such influencing nodes in online social networks on discovering surprising links in the existing ones of influencing nodes, which nodes in online social networks will be linked next from the attacked influencing nodes to monitor the risk. The results showed 42 influencing nodes of anomaly and attack patterns and can be predict 31 new links from such nodes were achieved by SVA Model with the accuracy of confidence level 95.0%. The new proposed model and results illustrated SVA Model was significance analysis. Such understanding can lead to efficient implementation of tools to links prediction in online social networks. They could be applied as a guide to further investigate of social networks behavior to improve the security model and notify the risk, computer viruses or cyber-attacks for online social networks in advance.

The rise of the Internet accelerates the creation of various large-scale online social networks. The online social networks have brought considerable attention as an important medium for the information diffusion model, which can be described the relationships and activities among human beings. The online social networks’ relationships in the real world are too big to present with useful information to identify the criminal or cyber attacks. The methodology for information security analysis was proposed with the complementary of Cluster Algorithm and Social Network Analysis, which presented anomaly and cyber attack patterns in online social networks and visualized the influencing nodes of such anomaly and cyber attacks. The closet vertices of influencing nodes could not avoid from the harmfulness in social networking. The new proposed information security analysis methodology and results were significance analysis and could be applied as a guide for further investigate of social network behavior to improve the security model and notify the risk, computer viruses or cyber attacks for online social networks in advance.