# ML-Based 5G Network Slicing Security:
## 8 Conclusions  
This paper extends the understanding of the impact of network slicing over 5G. This paper explains network slicing and its layers and architectural framework by using a different standardization. The state-of-the-art comparison and road-map of the existing surveys has been discussed in addition to the timeline of the evolution of slicing. The key aspects the paper presents are the importance of security in NS and how to minimize attacks that threaten the network. Furthermore, the paper demonstrates that machine and deep learning solutions should be applied at the planning and design, construction and deployment, monitoring, fault detection, and security stages of NS, and with different application parameters and network functions. This paper gives a detailed survey of NS in 5G, machine learning-based NS and attack prevention to maintain confidentiality, authenticity, accessibility, availability, and data integrity. Finally, this paper presents a taxonomy of 5G Network Slicing, security, and management along with future directives and recommendations which anticipate a further need for analysis and evolution required in network slice security to eradicate threats and fulfill the user's requirements. This paper has shown how AI and ML can be integrated into networks in order to build self-healing and self-upgrading networks.  
AI is already being integrated in networks, with the objective of minimising capital costs, improving network performance, and creating fresh revenue sources. Operators all around the globe are experiencing the advantages of AI integration in their networks. By the end of 2020, it was expected that more than half of service providers (53%) expected to have completely incorporated certain components of AI into their networks. With possibilities for leveraging machine learning and artificial intelligence to interact with 5G networks, some sectors are already scrambling to innovate with 5G. Sports, wireless virtual reality (VR), augmented reality (AR), live performances, self-driving vehicles, public safety and infrastructure, ATMs, medical equipment, remote control heavy machinery, and healthcare are just a few of the top technologies on this frontier. The upcoming industry will be surely benefited by this survey and it will help to provide a detailed study on machine learning-based approaches in 5G network slicing. This survey will also help researchers to gain knowledge and understand the facts in this field, and encourage them to further explore this field in order to benefit society and future generations.  
Conceptualization, A.J., R.D., G.C. and N.D.; methodology, A.J., R.D., G.C. and N.D.; validation, A.J., R.D., G.C., N.D., M.K.M. and P.L.; investigation, A.J., R.D., G.C., N.D., M.K.M. and P.L.; resources, G.C. and N.D.; Data curation, A.J., R.D. and G.C.; writing--original draft preparation, A.J., R.D. and G.C.; writing--review and editing, G.C. and N.D.; visualization, A.J., R.D., G.C., N.D., M.K.M. and P.L.; supervision, G.C., N.D., M.K.M. and P.L.; project administration, G.C. and N.D.; funding acquisition, N.D. All authors have read and agreed to the published version of the manuscript.  
This research has partially been funded by Danish Industry Foundation through project \"CIDI-- Cybersecure IoT in Danish Industry\" (project number 2018-0197).  
Not applicable.