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VANET alert endorsement using multi-source filters

Published: 24 September 2010 Publication History

Abstract

We propose a security model for Vehicular Ad-hoc Networks (VANETs) to distinguish spurious messages from legitimate messages. In this paper, we explore the information available in a VANET environment to enable vehicles to filter out malicious messages which are transmitted by a minority of misbehaving vehicles. More specifically, we introduce a message filtering model that leverages multiple complementary sources of information to construct a multi-source detection model such that drivers are only alerted after some fraction of sources agree. Our filtering model is based on two main components: a threshold curve and a Certainty of Event (CoE) curve. A threshold curve implies the importance of an event to a driver according to the relative position, and a CoE curve represents the confidence level of the received messages. An alert is triggered when the event certainty surpasses a threshold. We analyze our model and provide some initial simulation results to demonstrate the benefits.

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      cover image ACM Conferences
      VANET '10: Proceedings of the seventh ACM international workshop on VehiculAr InterNETworking
      September 2010
      106 pages
      ISBN:9781450301459
      DOI:10.1145/1860058
      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 ACM 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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      Published: 24 September 2010

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      Author Tags

      1. misbehavior detection
      2. vanet

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      • (2023)Autonomous Vehicles: Sophisticated Attacks, Safety Issues, Challenges, Open Topics, Blockchain, and Future DirectionsJournal of Cybersecurity and Privacy10.3390/jcp30300253:3(493-543)Online publication date: 5-Aug-2023
      • (2023)A Comprehensive Survey of V2X Cybersecurity Mechanisms and Future Research PathsIEEE Open Journal of the Communications Society10.1109/OJCOMS.2023.32391154(325-391)Online publication date: 2023
      • (2022)DRiVe: Detecting Malicious Roadside Units in the Internet of Vehicles With Low Latency Data IntegrityIEEE Internet of Things Journal10.1109/JIOT.2021.30978099:5(3270-3281)Online publication date: 1-Mar-2022
      • (2022)PIC: Preserving Data Integrity in UAV Assisted CommunicationIEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)10.1109/INFOCOMWKSHPS54753.2022.9798213(1-6)Online publication date: 2-May-2022
      • (2022)MaDe: Malicious Aerial Vehicle Detection using Generalized Likelihood Ratio TestICC 2022 - IEEE International Conference on Communications10.1109/ICC45855.2022.9838465(1-6)Online publication date: 16-May-2022
      • (2021)Reinforcement Algorithm based Jamming-Detection for Vehicular Ad Hoc Networks2021 IEEE Bombay Section Signature Conference (IBSSC)10.1109/IBSSC53889.2021.9673350(1-5)Online publication date: 18-Nov-2021
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      • (2020)Robust cooperative car-parking: implications and solutions for selfish inter-vehicular social behaviourHuman-centric Computing and Information Sciences10.1186/s13673-020-00243-910:1Online publication date: 26-Aug-2020
      • (2020)A Feedback-Driven Lightweight Reputation Scheme for IoV2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)10.1109/TrustCom50675.2020.00141(1060-1068)Online publication date: Dec-2020
      • (2020)Securing Vehicle-to-Everything (V2X) Communication PlatformsIEEE Transactions on Intelligent Vehicles10.1109/TIV.2020.29874305:4(693-713)Online publication date: Dec-2020
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