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dc.contributor.authorKosmanos, Dimitrios
dc.contributor.authorPappas, Apostolos
dc.contributor.authorAparicio-Navarro, Francisco J
dc.contributor.authorMaglaras, Leandros
dc.contributor.authorJanicke, Helge
dc.contributor.authorBoiten, Eerke Albert
dc.contributor.authorArgyriou, Antonios
dc.date.accessioned2019-08-16T08:58:48Z
dc.date.available2019-08-16T08:58:48Z
dc.date.issued2019
dc.identifier.citationKosmanos, D. et al. (2019) Intrusion Detection System for Platooning Connected Autonomous Vehicles. 4th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM 2019), which will be held on September 20-22, 2019 at the University of Piraeus, Greece.en
dc.identifier.urihttps://www.dora.dmu.ac.uk/handle/2086/18342
dc.description.abstractThe deployment of Connected Autonomous Vehicles (CAVs) in Vehicular Ad Hoc Networks (VANETs) requires secure wireless communication in order to ensure reliable connectivity and safety. However, this wireless communication is vulnerable to a variety of cyber atacks such as spoofing or jamming attacks. In this paper, we describe an Intrusion Detection System (IDS) based on Machine Learning (ML) techniques designed to detect both spoofing and jamming attacks in a CAV environment. The IDS would reduce the risk of traffic disruption and accident caused as a result of cyber-attacks. The detection engine of the presented IDS is based on the ML algorithms Random Forest (RF), k-Nearest Neighbour (k-NN) and One-Class Support Vector Machine (OCSVM), as well as data fusion techniques in a cross-layer approach. To the best of the authors’ knowledge, the proposed IDS is the first in literature that uses a cross-layer approach to detect both spoofing and jamming attacks against the communication of connected vehicles platooning. The evaluation results of the implemented IDS present a high accuracy of over 90% using training datasets containing both known and unknown attacks.en
dc.language.isoenen
dc.subjectIntrusion Detection Systemsen
dc.subjectConnected Autonomous Vehiclesen
dc.subjectVehicular Ad Hoc Networksen
dc.titleIntrusion Detection System for Platooning Connected Autonomous Vehiclesen
dc.typeConferenceen
dc.peerreviewedYesen
dc.funderOther external funder (please detail below)en
dc.cclicenceCC-BY-NCen
dc.date.acceptance2019-07-22
dc.funder.otherAirbusen


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