Efficient Internet-of-Things Cyberattack Depletion Using Blockchain-Enabled Software-Defined Networking and 6G Network Technology.

6G technology IoT blockchain technology consensus blockchain technology cyberattack edge computing software-defined networking virtual network function

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
07 Dec 2023
Historique:
received: 26 09 2023
revised: 09 11 2023
accepted: 29 11 2023
medline: 23 12 2023
pubmed: 23 12 2023
entrez: 23 12 2023
Statut: epublish

Résumé

Low-speed internet can negatively impact incident response by causing delayed detection, ineffective response, poor collaboration, inaccurate analysis, and increased risk. Slow internet speeds can delay the receipt and analysis of data, making it difficult for security teams to access the relevant information and take action, leading to a fragmented and inadequate response. All of these factors can increase the risk of data breaches and other security incidents and their impact on IoT-enabled communication. This study combines virtual network function (VNF) technology with software -defined networking (SDN) called virtual network function software-defined networking (VNFSDN). The adoption of the VNFSDN approach has the potential to enhance network security and efficiency while reducing the risk of cyberattacks. This approach supports IoT devices that can analyze large volumes of data in real time. The proposed VNFSDN can dynamically adapt to changing security requirements and network conditions for IoT devices. VNFSDN uses threat filtration and threat-capturing and decision-driven algorithms to minimize cyber risks for IoT devices and enhance network performance. Additionally, the integrity of IoT devices is safeguarded by addressing the three risk categories of data manipulation, insertion, and deletion. Furthermore, the prioritized delegated proof of stake (PDPoS) consensus variant is integrated with VNFSDN to combat attacks. This variant addresses the scalability issue of blockchain technology by providing a safe and adaptable environment for IoT devices that can quickly be scaled up and down to pull together the changing demands of the organization, allowing IoT devices to efficiently utilize resources. The PDPoS variant provides flexibility to IoT devices to proactively respond to potential security threats, preventing or mitigating the impact of cyberattacks. The proposed VNFSDN dynamically adapts to the changing security requirements and network conditions, improving network resiliency and enabling proactive threat detection. Finally, we compare the proposed VNFSDN to existing state-of-the-art approaches. According to the results, the proposed VNFSDN has a 0.08 ms minimum response time, a 2% packet loss rate, 99.5% network availability, a 99.36% threat detection rate, and a 99.77% detection accuracy with 1% malicious nodes.

Identifiants

pubmed: 38139535
pii: s23249690
doi: 10.3390/s23249690
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Ministry of Science and Higher Education of the Republic of Kazakhstan
ID : AP19674517
Organisme : National Research Foundation of Korea (NRF) grant
ID : NRF-2021R1F1A1063640
Organisme : Deanship of the University of Taif Saudi Arabia
ID : 00

Auteurs

Abdul Razaque (A)

School of Computing, Gachon University, Seongnam 13120, Republic of Korea.

Joon Yoo (J)

School of Computing, Gachon University, Seongnam 13120, Republic of Korea.

Gulnara Bektemyssova (G)

Department of Computer Engineering and Information System, International Information Technology University, Almaty 050000, Kazakhstan.

Majid Alshammari (M)

Computers and Information Technology College, Taif University, Taif 26571, Saudi Arabia.

Tolganay T Chinibayeva (TT)

Department of Computer Engineering and Information System, International Information Technology University, Almaty 050000, Kazakhstan.

Saule Amanzholova (S)

Department of Cybersecurity, International Information Technology University, Almaty 050000, Kazakhstan.

Aziz Alotaibi (A)

Computers and Information Technology College, Taif University, Taif 26571, Saudi Arabia.

Dauren Umutkulov (D)

Department of Computer Engineering and Information System, International Information Technology University, Almaty 050000, Kazakhstan.

Classifications MeSH