5G Network Slicing Security

Wu, Tin-Yu; Jie, Tey Fu

doi:10.1007/978-3-030-87049-2_28

Cited by 1 publication

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2) Adaptive security measures: The security protocols can be tailored for each slice, accommodating varied requirements ranging from low-latency, high-reliability slices to slices designed for mMTC. (3) Resource allocation: The dynamic resource allocation capabilities of network slicing also extend to security resources, allowing for real-time adjustments in response to detected threats [56][57][58][59].…”

Section: Network Slicing Securitymentioning

confidence: 99%

“…An adversary gaining access to one network slice could exploit inter-slice vulnerabilities, affecting other slices and possibly leading to cross-slice attacks. Therefore, securing slice isolation becomes critical [56][57][58][59].…”

Section: Fifth-generation Network Potential Vulnerabilitiesmentioning

confidence: 99%

“…Network slicing necessitates a more dynamic, programmable, and software-based environment, primarily facilitated by SDN and NFV. While these technologies provide flexibility and optimization capabilities, they also introduce software vulnerabilities that may be exploited by attackers [57,59]. The isolation between network slices is another crucial technical aspect.…”

Section: Analysis Of Inherent Vulnerabilities In Network Slicingmentioning

confidence: 99%

“…Vulnerabilities in network slicing in 5G networks and their associated mitigation strategies[56][57][58][59].…”

mentioning

confidence: 99%

See 3 more Smart Citations

A Comprehensive Study on the Role of Machine Learning in 5G Security: Challenges, Technologies, and Solutions

Fakhouri,

Alawadi,

Awaysheh

et al. 2023

Electronics

View full text Add to dashboard Cite

Fifth-generation (5G) mobile networks have already marked their presence globally, revolutionizing entertainment, business, healthcare, and other domains. While this leap forward brings numerous advantages in speed and connectivity, it also poses new challenges for security protocols. Machine learning (ML) and deep learning (DL) have been employed to augment traditional security measures, promising to mitigate risks and vulnerabilities. This paper conducts an exhaustive study to assess ML and DL algorithms’ role and effectiveness within the 5G security landscape. Also, it offers a profound dissection of the 5G network’s security paradigm, particularly emphasizing the transformative role of ML and DL as enabling security tools. This study starts by examining the unique architecture of 5G and its inherent vulnerabilities, contrasting them with emerging threat vectors. Next, we conduct a detailed analysis of the network’s underlying segments, such as network slicing, Massive Machine-Type Communications (mMTC), and edge computing, revealing their associated security challenges. By scrutinizing current security protocols and international regulatory impositions, this paper delineates the existing 5G security landscape. Finally, we outline the capabilities of ML and DL in redefining 5G security. We detail their application in enhancing anomaly detection, fortifying predictive security measures, and strengthening intrusion prevention strategies. This research sheds light on the present-day 5G security challenges and offers a visionary perspective, highlighting the intersection of advanced computational methods and future 5G security.

show abstract

Section: Network Slicing Securitymentioning

confidence: 99%