Purpose – The purpose of this paper is to develop a formal set of information technology (IT) governance practices based on sufficiency economy philosophy (SEP) to support the generic context for Thai universities. Design/methodology/approach – The research methodology in this study is divided into two main phases that are conceptualization and operationalization. In the phase of conceptualization, the authors reviewed literature related to the implementation of IT governance in universities and the principles of SEP in order to conceptualize an initial idea of IT governance on the basis of SEP. In the phase of operationalization, the authors performed in-depth interviews with the CIOs of 20 universities, five IT experts, and five SEP experts in order to verify the proposed concept. Findings – This study provides two key findings: the IT governance practices based on SEP for Thai universities and the mapping of IT governance practices based on SEP with ISO/IEC 38500. Practical implications – The total of 65 practices presented in this study can be used as a guideline for handling of IT governance issues in Thai universities. Originality/value – This study provides university IT governance practices based on the principles of SEP that is widely accepted and highly appreciated in Thailand.

Abstract-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.

Abstract-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.