[1] 1. TRUSTED CLOUD COMPUTING WITH SECURE RESOURCES AND DATA COLORING1.1 Trust and security are considered two major factors in cloud computing platform. security have prevented businesses from fully accepting cloud platforms. Trust and reputation management has made cloud computing reliable through secure resources, data coloring, and watermarking. A reputation system is one of the ways to establish trust between service providers and data owners. These techniques are used to protect confidential data from unauthorized access. A new business model is enabled by cloud computing that supports on-demand, pay-per-use computing services that enable economies of scale over the Internet. The cloud functions as a service factory built around virtualized data centers. Hardware, software, networks and data sets delivered with virtualization to build the cloud platforms dynamically. The basic idea is to migrate desktop computing to virtual server cluster and data centers. The Cloud ecological environment must be secure, trustworthy and independent. Trust and security must first be established to increase adoption of web and cloud services by cloud service providers (CPs). There should be no abuse, violation, hacking, cheating, rumors, viruses in a healthy cloud ecosystem. The main issue is establishing trust between Cloud users and CSPs and Cloud data owners. To manage these issues, a reputation-based trust management system, complemented by data coloring and software watermarking, is used. 1.2 Cyber ​​trust requirements in cloud services Some critical issues for trusted cloud computing, and several recent works discuss general issues on cloud security. and privacy was identified by Cloud Computing...... middle of paper ......e true, the probability that p4 is true is 0.8; in all other CASES, THE PROBABILITY IS 0 (P4 IS FALSE). In enterprise networks, graphical modes are important tools for analyzing security events. It may seem that the combination of Bayesian network and attack graph does not work well together, the real semantics and inference power of Bayesian networks are not fully utilized by doing this. Bayesian networks are used very appropriately. To use Bayesian networks, we must identify and correctly represent the relevant uncertainties. The Bayesian networks model is used to capture uncertain relationships, and experimental results show that the use of Bayesian networks can provide new opportunities to improve enterprise security analysis. To the best of our knowledge, our work is the first effort that investigates systematic approaches to combining attack graphs and Bayesian networks.