Cloud computing (CC) is becoming an essential technology worldwide. This approach represents a revolution in data storage and collaborative services. Nevertheless, security issues have grown with the move to CC, including intrusion detection systems (IDSs). Intruders have developed advanced tools that trick the traditional IDS. This study attempts to contribute toward solving this problem and reducing its harmful effects by boosting IDS performance and efficiency in a cloud environment. We build two models based on deep neural networks (DNNs) for this study: the first model is built on a multi-layer perceptron (MLP) with backpropagation (BP), and the other is trained by MLP with particle swarm optimization (PSO). We use these models to deal with binary and multi-class classification on the updated cybersecurity CSE-CIC-IDS2018 dataset. This study aims to improve the accuracy of detecting intrusion attacks for IDSs in a cloud environment and to enhance other performance metrics. In this study, we document all aspects of our experiments in depth. The results show that the best accuracy obtained for binary classification was 98.97% and that for multi-class classification was 98.41%. Furthermore, the results are compared with those from the related literature.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.