Chronological salp swarm algorithm based deep belief network for intrusion detection in cloud using fuzzy entropy

Cloud computing is susceptible to the existing information technology attacks, as it extends and uses the traditional operating system, information technology infrastructure, and applications. However, in addition to the existing threats, the cloud computing environment faces various security issues in detecting anomalous network behaviors. In order to resolve the security issues, an effective intrusion detection system named Chronological Salp Swarm Algorithm-based Deep Belief Network is proposed for detecting the suspicious intrusions in a cloud environment. Accordingly, the proposed Chronological Salp Swarm Algorithm-based Deep Belief Network is developed by integrating the Chronological concept with the Salp Swarm Algorithm. The optimal solution for detecting the intrusion is revealed using the fitness function, which accepts the minimal error vale as the optimum solution. Here, the weights are optimally tuned by the proposed algorithm to generate an effective and optimal solution for detecting the intruders. The proposed Chronological Salp Swarm Algorithm-based Deep Belief Network obtained better performance through the facility of exploitation and the exploration in search space. The performance of the proposed method is analyzed using two datasets, namely KDD cup dataset and BoT-IoT dataset, the comparative analysis is performed with the existing methods, such as Host based intrusion detection system, Deep learning, and Deep Neural Network + Genetic Algorithm. The proposed Chronological Salp Swarm Algorithm-based Deep Belief Network obtained better performance in terms of accuracy, sensitivity, and specificity, with the values of 0.9618%, 0.9702%, and 0.9307% using KDD cup dataset, and 0.9764%, 0.9824%, and 0.9309% using BoT-IoT dataset.