Distributed Collaborative Anomaly Detection for Trusted Digital Twin Vehicular Edge Networks

Jiawen Liu, Shuaipeng Zhang, Hong Liu, Yan Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

The vehicular networks are vulnerable to cyber security attacks due to the vehicles’ large attack surface. Anomaly detection is an effective means to deal with this kind of attack. Due to the vehicle’s limited computation resources, the vehicular edge network (VEN) has been proposed provide additional computing power while meeting the demand of low latency. However, the time-space limitation of edge computing prevents the vehicle data from being fully utilized. To solve this problem, a digital twin vehicular edge networks (DITVEN) is proposed. The distributed trust evaluation is established based on the trust chain transitivity and aggregation for edge computing units and digital twins to ensure the credibility of digital twins. The local reachability density and outlier factor are introduced for the time awareness anomaly detection. The curl and divergence based elements are utilized to achieve the space awareness anomaly detection. The mutual trust evaluation and anomaly detection is implemented for performance analysis, which indicates that the proposed scheme is suitable for digital twin vehicular applications.

Original languageEnglish
Title of host publicationWireless Algorithms, Systems, and Applications - 16th International Conference, WASA 2021, Proceedings
EditorsZhe Liu, Fan Wu, Sajal K. Das
PublisherSpringer Science and Business Media Deutschland GmbH
Pages378-389
Number of pages12
ISBN (Print)9783030861292
DOIs
StatePublished - 2021
Event16th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2021 - Nanjing, China
Duration: 25 Jun 202127 Jun 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12938 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2021
Country/TerritoryChina
CityNanjing
Period25/06/2127/06/21

Keywords

  • Anomaly detection
  • Digital twin
  • Trust evaluation

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