# A Deep Learning Approach for Intrusion Detection Systems in Cloud Computing Environments
## 1 Introduction  
In the world of information technology, cloud computing is now regarded as one of the greatest computing paradigms in recent years. The development of existing computer paradigms, such as parallel computing, grid computing, distributed computing, and cloud computing, can be considered the best among these models [1]. Cloud computing provides customers with a variety of computing resources and online storage. For both businesses and individuals, cloud services have recently become crucial [2]. In agreement with the National Institute of Standards and Technology (NIST), cloud computing is defined as \"a model for convenient, on-demand network access to a shared pool of configurable computing resources (networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction\" [3]. In order to better understand the security issues related to cloud computing and its services, we first need to understand the configuration of cloud computing. Based on NIST's description, \"five essential characteristics, three service models, and four deployment models make up this cloud model\", which will be discussed in the background section. The security of cloud computing is of prime importance since it is used in many critical areas, such as education systems, healthcare systems, and e-government, which store important information. However, cloud computing potentially faces security threats if a network is compromised by an attacker [4]. Additionally, the Internet is utilized for providing access to cloud services, making it a significant source of vulnerabilities that can infect cloud resources and services. Consequently, improving cloud security becomes the primary challenge for cloud providers [5]. However, several cybersecurity methods, such as firewalls, antivirus software, data encryption algorithms, intrusion detection systems (IDSs), intrusion prevention systems (IPS), and others, have been used to provide security to the cloud environment from internal and external malicious attacks [6]. Although a variety of traditional mechanisms of defense are used to protect and detect attacks in the cloud-based network, there remains significant and continuous growth in threats and attacks due to the relentless evolution of cyberattacks [7]. Consequently, in order to increase security, this has resulted in a paradigm shift toward more intelligent and adaptive mechanisms, exemplified by machine learning (ML) and its subdomain, deep learning (DL), rather than traditional mechanisms of defense [8]. Intrusion detection systems (IDSs) in cloud computing earned a lot of attention during the past few years as one of the most effective ways to detect and recognize malicious activity [9]. In cybersecurity, an intruder is an entity that seeks to exploit system weaknesses [10]. Therefore, an IDS is used to detect an intrusion in a network, monitor all packets, and decide whether any of the incoming and outgoing packets have been affected by the intrusion [11]. Intrusion detection can be accomplished using one of two methods. The first involves traditional signature-based intrusion detection systems. However, these systems have limitations in detecting novel attacks and have not been suitable enough for large network scales such as cloud computing environments. Intelligent intrusion detection systems, on the other hand, use an anomaly-based approach based on learning methods [12]. This allows them to detect novel and unknown attacks by leveraging machine learning and deep learning models' ability to learn intricate traffic patterns and accurately pinpoint the nature of the attack [13]. Nowadays, the use of machine learning in cybersecurity has become popular, and cybersecurity researchers are relentless in ensuring that state-of-the-art cybersecurity mechanisms are achieved.