Threat Modeling for ML-based Topology Prediction in Vehicular Edge Computing Architecture

Hong Hanh Doan, Audri Adhyas Paul, Harald Zeindlinger, Yiheng Zhang, Sajjad Khan, Davor Svetinovic

Publication: Chapter in book/Conference proceedingContribution to conference proceedings

Abstract

The Internet of Vehicles (IoV), a network that interlinks vehicles, infrastructure, and assorted entities, serves as a cornerstone for intelligent transportation systems and the emergence of smart cities. Within this context, edge computing has been identified as a critical solution for providing rapid and reliable data processing. Machine Learning (ML) techniques have become essential to IoV activities such as resource allocation and load balancing across mobile edge servers, typified by decentralized services that range from natural language processing to image recognition. The fusion of ML with edge computing within IoV architecture promises enhanced performance, efficiency, and safety. However, this amalgamation also creates challenges related to data privacy, cybersecurity, malfunctioning edge devices, inconsistent network connectivity, human errors, and malicious insiders. Consequently, this paper focuses on modeling security threats within an ML-based edge computing framework for the IoV. We analyze the system provided by the Linux Foundation Edge Akraino Project's Stable Topology Prediction blueprint by employing a hybrid threat modeling technique. Our strategy leverages STRIDE to elicit threats on distinct system elements like vehicle-to-vehicle communication networks, edge networks, and ML models. Subsequently, these threats are consolidated for a comprehensive view using an attack tree.
Original languageEnglish
Title of host publication2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
Subtitle of host publication14-17 November 2023 : Abu Dhabi, United Arab Emirates
EditorsMoayad Aloqaily
Place of PublicationNew York
PublisherIEEE
Pages523 - 530
ISBN (Electronic)979-8-3503-0460-2
ISBN (Print)979-8-3503-0461-9
DOIs
Publication statusPublished - 25 Dec 2023

Publication series

SeriesIEEE Xplore
ISSN2837-0724

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 102025 Distributed systems

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