Abstract
Distributed computing applications provide concurrent processing and services executed from different systems through a common cloud platform. However, without modifications or adaptable security measures, such concurrency presents a great challenge to the proper administration of security. This paper introduces a hybrid secure equivalent computing model to address this security issue. The proposed security model was designed using a genetic algorithm for equivalent measure distribution over the processing systems. Via this model, variations in security management owing to differences in the processing times of various services can be mitigated using the probabilistic annealing method. This method helps to preserve the stability of the security method without decreasing its robustness. For robust processing, the model exploits parallel security as a service feature from the cloud with a non-tokenized key sharing method. The key sharing and revocation processes are determined using the probabilistic outcomes of the annealing method. The genetic process verifies the distribution of security measures in the key sequence of any possible processing combination without compromise. The performance of the proposed model was verified using the metrics of process failure, computational complexity, time delay, false rate, and computing level.