## 1 Introduction  
Cloud computing (CC) is familiar and is obtaining attention from individual users and organizations. However, the transition to the cloud platform could be more direct, and there are many operational and security difficulties [1]. Guaranteeing the security of information outsourcing to the cloud is significant because of the trend of storing more data in the cloud. Virtual machines (VMs) and hypervisor technologies are also a security threat, as these VM and hypervisor technologies are prone to VM-level attacks [2, 3]. Such a mechanism has some on-site computer institutions with more software and hardware systems [4, 5]. Susceptibilities in the VM structure are used by invaders for exfiltrating data or conducting assaults, such as distributed denial of service (DDoS). This occurs because of the inherent weaknesses in the internet protocol stack. Moreover, numerous novel assaults have recently appeared that use metamorphosis and polymorphism to avoid recognition [6]. In an IaaS cloud atmosphere, data regarding the victims' machines are easily exploited and acquired, enabling assaults on VMs. The cloud structure is open and fully dispersed, making it a vulnerable target for invaders [7; 8]. Thus, the security of cloud platforms is at risk, where conventional network assaults, along with cloud-specific assaults, threaten cloud users (may be organizations or individuals). Conventional network security measures and firewalls are better for stopping several outsider assaults. Still, assaults within the network, along with certain complex outsider assaults cannot be managed efficiently through these systems [9]. This is where intrusion detection systems (IDSs) come into play. The role of IDS in the security of the cloud is highly significant, as it serves as an additional defensive layer of safety. Apart from identifying only familiar assaults, it can find variants of unknown and familiar assaults [10; 11]. An intrusion is an attempt to bypass the security mechanism or to negotiate the confidentiality, integrity, and availability (CIA) of a network or computer [12]. Intrusions are activated by aggressors trying to access cloud resources over the Internet, legal users trying to obtain privileges not provided to them formally, and users who misuse their rights to access resources [13]. Intrusion detection (ID) observes the actions in a network or computer and scrutinizes them for signs of intrusion. IDS could be hardware or software, or an integration of both to automate the ID process [14]. It captures data from the network or under observation and informs the network manager by logging or mailing intrusion events. However, the warnings made by IDS are rarely related to actual intrusion because false positives and negatives influence the IDS performance [15]. Though several IDS models are available in the literature, improvement is still needed for the detection efficiency. In addition, feature selection (FS) techniques are essential to eliminate irrelevant features that raise false alarms and increase the accuracy of the system. This paper presents improved metaheuristics with a fuzzy logic-based intrusion detection system for the cloud security (IMFL-IDSCS) technique. The IMFL-IDSCS technique uses the enhanced chimp optimization algorithm-based feature selection (ECOA-FS) method for choosing optimal features. The adaptive neuro-fuzzy inference system (ANFIS) method is enforced to recognize intrusions. Finally, the hybrid java shark smell optimization (JSSO) algorithm is used to optimize the membership functions (MFs). A widespread simulation analysis is performed to examine the enhanced outcomes of the IMFL-IDSCS technique. In short, the major contributions of the study are given as follows:  
* An intelligent IMFL-IDSCS technique comprising ECOA-FS, ANFIS classification, and JSSO-based parameter optimization is presented for intrusion detection. To the best of our knowledge, the presented IMFL-IDSCS technique does not exist in the literature.