# Deep Learning-Based Symptomizing Cyber Threats Using Adaptive 5G Shared Slice Security Approaches
## 1 Introduction  
The phenomenon of KPIs in 5G networks specifies the functionality from the access level to the core network, to supporting critical network functions (NFs) [1, 2]. Network slicing has been developed as a special logical and virtualized network on top of the multi-domain physical structure, enabling the coexistence of many verticals \"Slices\" over the same infrastructure. Each network slice has functional resources that are either dedicated, shared, or both; separated from other network slices; and have useful potential based on the requirement of each slice. Network slicing can be incorporated into the core network (CN) using both software defined networking (SDN) and network function virtualization (NFV). NFV and SDN demonstrate how software functions can be connected to share physical resources and can be operated in a virtual environment. Among new network security challenges, 5G slice security has not yet been explored [3]. A rule-based slice-service-type (SST) security system is generally initiated on 5 Gnetworks. The physical resources of the network are shared among users or enterprises and can be managed in a secure environment using virtualization. The process of the slice initiating or embedding with resources during its execution cycle causes many loopholes in configuration, malware injection, or telnet session misconfiguration, which can help hackers to compromise the shared resources in a 5G environment. Moreover, legitimate tenants (users and enterprises) could facilitate the penetration of attacks while using suspicious third party services in a 5G network [4; 5; 6; 7; 8]. The auto-negotiation and intra-communication of shared resources with distinct security measures is also an emerging phenomenon in 5G slicing [9; 10]. In the context of 5G slice resiliency and security control, to what extent can shared network resources can be managed, preserved in quarantine, and kept safe from paired suspicious resources? Hence, the attack vectors are multi-directional and can be dispersed in networks more swiftly [8; 11]. To mitigate or suppress such attack vectors before activation, much effort has taken place in the field of AI using DL security modules, following proactive security schemes to optimize slices [12; 13]. Further, the placement of security modules or NFs is a challenging task. Points of presence (PoPs) of security scanners and their integration can reduce the operating expenditure (Opex) and provide better QoS in a 5G virtual infrastructure. Virtual security as a service (Vsaas) is an upcoming challenge, and its integration and placement in virtual slices can facilitate VNF operators and provide a reliable solution to the dynamic security deployment of 5G and B5G networks [14; 15]. When talking about speedy networks, e.g., 5G networks (high data rates and quick convergence) can encounter AI multi-vector attacks. While traditional defense systems fail to block such attacks because of their obsolete and stationary architecture [16], conventional security systems become overloaded and drop packets due to large data. Additionally, parallel techniques, e.g., FPGA and ASIC, have been adopted to deal with AI attacks, but these cutting-edge technologies are bound by hardware limitations. For zero-day attack detection, DL models have been proven as the most superior forms and make a significant contribution to 5G and Beyond 5G (B5G) infrastructure security and robustness. Designing a diversified security system with an advanced architectural approach is more challenging than ever before. Considering essential security measures to safeguard the cyber world through the strong, effective, and adaptive security steps can prove more sustainable and proactive in the real-time identification of attack vectors in modern networks with logical reasoning.