Sökning: LAR1:hh
> Högskolan i Halmstad
> Blekinge Tekniska Högskola >
A Comprehensive Stu...
A Comprehensive Study on the Role of Machine Learning in 5G Security : Challenges, Technologies, and Solutions
-
- Fakhouri, Hussam N. (författare)
- University Petra, Amman, Jordan,University of Petra, Jordan
-
- Alawadi, Sadi, 1983- (författare)
- Blekinge Tekniska Högskola,Institutionen för datavetenskap
-
- Awaysheh, Feras M. (författare)
- Tartu University, Tartu, Estonia
-
visa fler...
-
- Bani Hani, Imad, 1982- (författare)
- Högskolan i Halmstad,Akademin för informationsteknologi,Halmstad University
-
- Alkhalaileh, Mohannad (författare)
- Al Ain University, Al Ain, Förenade Arabemiraten,Al Ain University, United Arab Emirates
-
- Hamad, Faten (författare)
- Sultan Qaboos University, Muscat, Oman; University Jordan, Amman, Jordan
-
visa färre...
-
(creator_code:org_t)
- Basel : MDPI, 2023
- 2023
- Engelska.
-
Ingår i: Electronics. - Basel : MDPI. - 2079-9292. ; 12:22, s. 1-44
- Relaterad länk:
-
https://doi.org/10.3...
-
visa fler...
-
https://bth.diva-por... (primary) (Raw object)
-
https://urn.kb.se/re...
-
https://doi.org/10.3...
-
https://urn.kb.se/re...
-
visa färre...
Abstract
Ämnesord
Stäng
- 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. © 2023 by the authors.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)
Nyckelord
- 5G networks
- machine learning security
- security in deep learning
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas