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A Novel Efficient Dynamic Throttling Strategy for Block-chain-Based Intrusion Detection Systems in 6G-Enabled VSNs

Alevizos, L, Thong Ta, V and Hashem Eiza, M (2023) A Novel Efficient Dynamic Throttling Strategy for Block-chain-Based Intrusion Detection Systems in 6G-Enabled VSNs. Sensors, 23 (18). ISSN 1424-8220

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Vehicular Social Networks (VSNs) have emerged as a new social interaction paradigm, where vehicles can form social networks on the roads to improve the convenience/safety of passengers. VSNs are part of Vehicle to Everything (V2X) services, which is one of the industrial verticals in the coming sixth generation (6G) networks. The lower latency, higher connection density, and near-100% coverage envisaged in 6G will enable more efficient implementation of VSNs applica-tions. The purpose of this study is to address the problem of lateral movements of attackers who could compromise one device in a VSN, given the large number of connected devices and services in VSNs and attack other devices and vehicles. This challenge is addressed via our proposed Blockchain-based Collaborative Distributed Intrusion Detection (BCDID) system with a novel Dynamic Throttling Strategy (DTS) to detect and prevent attackers’ lateral movements in VSNs. Our experiments showed how the proposed DTS improve the effectiveness of the BCDID system in terms of detection capabilities and handling queries three times faster than the default strategy with 350k queries tested. We concluded that our DTS strategy can increase transaction processing capacity in the BCDID system and improve its performance while maintaining the integrity of data on-chain.

Item Type: Article
Uncontrolled Keywords: 0301 Analytical Chemistry; 0502 Environmental Science and Management; 0602 Ecology; 0805 Distributed Computing; 0906 Electrical and Electronic Engineering; Analytical Chemistry
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Publisher: MDPI
SWORD Depositor: A Symplectic
Date Deposited: 20 Sep 2023 14:28
Last Modified: 04 Oct 2023 11:45
DOI or ID number: 10.3390/s23188006
URI: https://researchonline.ljmu.ac.uk/id/eprint/21522
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