RETRACTED ARTICLE: A novel centralized cloud information accountability integrity with ensemble neural network based attack detection approach for cloud data

Abstract

Highly scalable services are enabled by cloud computing and easily consumed over the Internet. User data is normally stored remotely in cloud services. Users do not own or operate the machines. This is a key feature of cloud services. Adoption of cloud services by the users are affected due to the fact that the users’ concerns about having to lose control on their own information and some attackers will hack them. Therefore to overcome the existing issues, this work proposed a new centralized cloud information accountability integrity with imperialist competitive key generation algorithm (CCIAI-ICKGA) is to resolve the above problems. It also provides the attack detection to monitor the practical utilization of the users’ information in the cloud environment. Second, cipher text-policy attribute-based encryption (CP-ABE) with key generation employing ICKGA and trapdoor generator is used to generate the public and private keys for every user. Third, the trapdoor generator ensures data integrity at the user level and also the cloud server level. At last, a dynamically weighted ensemble neural networks (DWENN) classifier is used for attack detection in the network. Additional guarantees of integrity and authenticity are provided by updating the structure of the log records. It is extended the framework in order to provide the security analysis which detects more possible attacks. Finally, a simulation result is carried out and renders a detailed performance analysis of the system. A result from rigorous experimental evaluation demonstrates the efficacy and resourcefulness of the novel CCIAI-ICKGA framework.