Cloud computing is vulnerable to accessible Information Technology (IT) attacks, as it expands as well as exploits conventional OS, IT infrastructure as well as applications. Nevertheless, the cloud computing environment occurs several security problems in recognizing the anomalous network behaviors with respect to the existing threats. An effectual Intrusion Detection System (IDS) called a hybrid Grasshopper Optimization (GSO) algorithm with Bat Algorithm (BA)- based DBN is developed to identify suspicious intrusions in cloud environments in order to solve security problems. By exploiting the fitness function the optimal solution to detect the intrusion is shown that recognizes the minimum error value as the optimal solution. Moreover, using adopted optimization approach is used to tune the weights optimally to produce an effective and best solution to detect the intruders. Nevertheless, the adopted optimization model-based Deep Belief Network (DBN) attained superior performance regarding the accuracy, sensitivity, as well as specificity by exploiting the BoT-IoT dataset.
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