## 1 Introduction
A centralized security policy can protect all enterprise endpoints from external threats and can be investigated through authentication. All network layers must be covered by comprehensive end-to-end security strategies for 5G networks. Fifth-generation network operators need to have full visibility, control, and monitoring of overall network layers to implement such a thorough security mechanism. To manage and control security policies, open application program interfaces (APIs) should be combined with 5G technologies. Figure 1: Evolved security requirements for 5G technologies. The 5G network can thus have uniform software and hardware security policies. The implementation of the security mechanism in new 5G services will become easier with high visibility across the network. Furthermore, improved visibility helps in preventing and isolating the threats before attacks happen [7]. However, most research focuses on the three core requirements for 5G security, the CIA [10; 11; 12]. Figure 2 presents the core requirements for 5G security and the solutions to maintain these requirements. A comprehensive security policy is the starting point for managing the countermeasures needed to secure wireless networks. Technical countermeasures that help in wireless security environments include hardware and software. Hardware countermeasures like smart cards, VPNs, and biometrics are hardware solutions while proper AP configuration, software patches, authentication, intrusion detection systems (IDS), and encryption are all examples of software countermeasures [13]. Researchers have put forward suggestions based on cybersecurity defensive systems, particularly the well-known IDS, to protect against cyber threats from wireless communications by anticipating and resolving flaws. Due to the heterogeneity and massive amounts of unstructured data present in the network, IDS procedures are useless for the real-time detection of potential intrusions in 5G [14]. One of the most promising methods in the field of artificial intelligence (AI) over the past few years has been machine learning (ML). Its remarkable abilities enable systems to learn from enormous amounts of data, considerably more than humans can process. Organizations can use ML to evaluate large datasets, improve their understanding, and provide accurate predictions. With its unique ability to predict and avert issues, this disruptive technology opens the door for more proactive and effective approaches in a variety of fields [15; 16; 17]. The machine learning field has enabled different paths that are effective in handling network intruders. Therefore, the employment of ML tools in 5G systems has attracted much interest from international projects and research, such as in [18; 19; 20; 21]. ML techniques can examine the features of the network data to distinguish between attacks and normal traffic. Some attributes improve the accuracy of the intrusion detection system, whereas the network data contains noisy attributes as well that decrease the detection accuracy. Feature selection techniques are considered crucial for an IDS because FS helps in increasing the accuracy of detection, which is used as the input to the learning approaches. A reliable intrusion detection system must be implemented because the IEEE 802.11 protocol-based short-distance transmission wireless network has faced security issues. Many researchers have proposed various IDS systems, and it has been found that data-mining-based techniques are very effective at detecting abnormal network behavior in these systems [22]. Intrusion detection has new difficulties as network data keeps growing exponentially; these difficulties are caused by the data's nonlinear structure and enormous volume. The characteristics of the data, notably the existence of redundant features, have a  
Figure 2: Core requirements for 5G security (**left**) and solutions to maintain system security (**right**). substantial impact on the efficacy of existing approaches.