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Anomaly Detection Framework for Securing Next Generation Networks of Platoons of Autonomous Vehicles in a Vehicle-to-Everything System

Authors:

Mustafa AbdallahAuthors Info & Claims

CPSS '23: Proceedings of the 9th ACM Cyber-Physical System Security Workshop

Pages 24 - 35

https://doi.org/10.1145/3592538.3594274

Published: 19 July 2023 Publication History

Abstract

We consider a security setting involving a platoon of autonomous vehicles (AVs) that commute from one place to another. Such vehicle platooning is utilized to optimize the usage and safety of highways. We propose a dynamic framework for a network of platoons that captures both the communication between different platoons along with the communication between different AVs within the single platoon. We propose an authenticity score scheme for monitoring the behavior of the platoons. We also propose a two-phase anomaly detection within a single platoon to elect and maintain a benign platoon leader. We then propose a long-short term memory (LSTM)-based RSU level anomaly detection scheme to safeguard the whole network of platoons. Finally, we adapt group-based signatures and channel switching schemes for ensuring that the communication channels between AVs and platoons stay secure against man-in-the-middle and denial of service attacks. We perform extensive numerical simulations to evaluate the different components in our framework.

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Cited By

Nazat SArreche OAbdallah M(2024)On Evaluating Black-Box Explainable AI Methods for Enhancing Anomaly Detection in Autonomous Driving SystemsSensors10.3390/s2411351524:11(3515)Online publication date: 29-May-2024
https://doi.org/10.3390/s24113515
Herman Muraro Gularte KAlfredo Ruiz Vargas JPaulo Javidi da Costa JSantos da Silva AAlmeida Santos GWang YAlfons Müller CLipps CTimóteo de Sousa Júnior Rde Britto Vidal Filho WSlusallek PDieter Schotten H(2024)Safeguarding the V2X Pathways: Exploring the Cybersecurity Landscape Through Systematic ReviewIEEE Access10.1109/ACCESS.2024.340294612(72871-72895)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3402946
Nazat SLi LAbdallah M(2024)XAI-ADS: An Explainable Artificial Intelligence Framework for Enhancing Anomaly Detection in Autonomous Driving SystemsIEEE Access10.1109/ACCESS.2024.338343112(48583-48607)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3383431
Show More Cited By

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Anomaly Detection Framework for Securing Next Generation Networks of Platoons of Autonomous Vehicles in a Vehicle-to-Everything System

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Published In

cover image ACM Conferences

CPSS '23: Proceedings of the 9th ACM Cyber-Physical System Security Workshop

July 2023

77 pages

ISBN:9798400700903

DOI:10.1145/3592538

Editors:
Rodrigo Roman
University of Malaga, Spain
,
Mujeeb Ahmed
, Newcastle University, UK

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 19 July 2023

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ASIA CCS '23

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ASIA CCS '23: ACM Asia Conference on Computer and Communications Security

July 10 - 14, 2023

VIC, Melbourne, Australia

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Cited By

Nazat SArreche OAbdallah M(2024)On Evaluating Black-Box Explainable AI Methods for Enhancing Anomaly Detection in Autonomous Driving SystemsSensors10.3390/s2411351524:11(3515)Online publication date: 29-May-2024
https://doi.org/10.3390/s24113515
Herman Muraro Gularte KAlfredo Ruiz Vargas JPaulo Javidi da Costa JSantos da Silva AAlmeida Santos GWang YAlfons Müller CLipps CTimóteo de Sousa Júnior Rde Britto Vidal Filho WSlusallek PDieter Schotten H(2024)Safeguarding the V2X Pathways: Exploring the Cybersecurity Landscape Through Systematic ReviewIEEE Access10.1109/ACCESS.2024.340294612(72871-72895)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3402946
Nazat SLi LAbdallah M(2024)XAI-ADS: An Explainable Artificial Intelligence Framework for Enhancing Anomaly Detection in Autonomous Driving SystemsIEEE Access10.1109/ACCESS.2024.338343112(48583-48607)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3383431
Sheik AMaple CEpiphaniou GDianati M(2023)A Comprehensive Survey of Threats in Platooning—A Cloud-Assisted Connected and Autonomous Vehicle ApplicationInformation10.3390/info1501001415:1(14)Online publication date: 25-Dec-2023
https://doi.org/10.3390/info15010014

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