## 1 Introduction  
Over the last decade, we have seen considerable advances in CC. Today, CC services have rich features, such as resource pooling and large networks. Cloud providers offer a platform, services, applications, and infrastructure to the cloud's users. The concept of a decentralized cloud is changing the face of the internet and decreasing the need for hardware and software. On the user side, the only requirement is being able to navigate the user interface of the CC system. Today, cloud technology is rapidly growing with a considerably increasing number of users, cloud service providers, and threats that target users and providers. As a result, the uploading of sensitive data to cloud storage by users poses many security issues regarding accessibility, availability, confidentiality, and integrity. Moreover, without perfect protection, cloud services' uninterrupted provision leads to a risk of intrusion [1]. The cloud environment is vulnerable to intrusion attacks because of its distributed nature. An IDS can be used to ensure the security of the CC by automatically checking the logs, network flow, and configurations. Nevertheless, traditional IDSs that are host- or network-based are not practical for CC since these IDSs are unable to detect hidden attack paths. This makes traditional IDSs inconvenient for CC because they are not designed with the specific context of CC to provide the necessary protection in such an environment. As a result, using traditional IDS techniques in the cloud environment leads to problems that do not exist in the conventional CC [2]. The two main methods of IDS based on detection are misuse (signature) and anomaly. In anomaly, a profile is created by recording normal behaviors, and then anomalies are determined from the regular pattern that can be marked as intrusions. Signature (misuse) detection is achieved with a pattern relating to known attacks or signatures of vulnerabilities. When the IDS detects malicious code matching these signatures, it raises the alarm and marks this code as an intrusion [2]. The vulnerabilities of IDS in the cloud environment can be solved using multiple evolutionary computing and programming techniques. Therefore, current strategies are geared toward the use of a combination of several methods together in one system to build robust and efficient IDSs [3]. Currently, researchers in this field are experimenting with novel methods using metaheuristic algorithms, which attempt to combine the benefits of these algorithms and artificial neural networks (ANNs) to mitigate any significant downside of IDSs [4]. Over the past few years, the applications of DNNs and metaheuristic optimization algorithms have become quite effective. Furthermore, there are different methods for using optimization algorithms in the IDSs. One of these methods uses the PSO algorithm, which simulates self-learning and social organization in groups of animals, such as birds and fish while finding food [5]. BP is a widely used algorithm for training MLP feedforward neural networks in order to adjust the model's weights and biases. Due to its capacity for learning and flexibility, it has been effectively used in many applications [6]. Furthermore, the PSO algorithm has a powerful capacity for exploitation as well as a quick speed of convergence [7]. Furthermore, using the PSO with neural networks gives better outcomes for IDSs [1]. This study attempts to contribute the previous efforts of researchers in this field using different strategies and an updated dataset to improve the performance of IDSs. The contributions of this study are summarized as follows:  
* We performed a comprehensive empirical study on IDSs using MLP-BP and MLP-PSO techniques to enhance performance metric scores and determine which model is more powerful in the cloud environment. * We tried to provide a general analysis of the CSE-CIC-IDS-2018 dataset, and we used it to validate the proposed models for both binary and multi-class classification.