Cloud computing belongs to a set of policies, protocols, technologies through which one can access shared resources such as storage, applications, networks, and services at relatively low cost. Despite the tremendous advantages of cloud computing, one big threat which must be taken care of is data security in the cloud. There are a dozen of threats that we are being exposed to while availing cloud services. Insufficient identity and access management, insecure interfaces and Applications interfaces (APIs), hijacking, advanced persistent threats, data threats, and many more are certain security issues with the cloud platform. APIs and service providers face a huge challenge to ensure the security and integrity of both network and data. To overcome these challenges access control mechanisms are employed. Traditional access control mechanisms fail to monitor the user operations on the cloud platform and are prone to attacks like IP spoofing and other attacks that impact the integrity of the data. For ensuring data integrity on cloud platforms, access control mechanisms should go beyond authentication, identification, and authorization. Thus, in this work, a trust-based access control mechanism is proposed that analyzes the data of the user behavior, network behavior, demand behavior, and security behavior for computing trust value before granting user access. The method that computes the final trust value makes use of the fuzzy logic algorithm. The trust value-based policies are defined for the access control mechanism and based on the trust value outcome the access control is granted or denied.
Cloud computing needs service provider with reliable communication for increasing the user trust. As existence of cloud depends on quality of services, evaluation of this trust value needs to be carried out by the cloud. Many of the web services provided by E-commerce, social sites, digital platform maintain this for the faith of user by estimating the reliability of service provider. This paper focuses on a model that can identify real nodes by its behavior in cloud. Here fuzzy max interval values have been evaluated from the transactional behavior of the node in fixed interval. By increase in transaction count, trust value of real node trust increases and trust value of malicious nodes decreases. The work is based on Role based Access Control (RBAC), which has three type of roles (Admin, Data owner, Node). Data owner content security was achieved by AES algorithm and only trusted node can access those resources. Experiment was performed by carrying out simulations on ideal and environment under attack. Analysis of evaluation parameters values shows that proposed model of fuzzy max interval trust is better as compared to other existing Domain Partition Trust Model (DPTM), for identification of malicious nodes.
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