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AI-Based Malicious Network Traffic Detection in VANETs
Halmstad University, Sweden.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0002-6032-6155
University of Liège, Belgium.
Halmstad University, Sweden.
2018 (engelsk)Inngår i: IEEE Network, ISSN 0890-8044, E-ISSN 1558-156X, Vol. 32, nr 6, s. 15-21Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Inherent unreliability of wireless communications may have crucial consequences when safety-critical C-ITS applications enabled by VANETs are concerned. Although natural sources of packet losses in VANETs such as network traffic congestion are handled by decentralized congestion control (DCC), losses caused by malicious interference need to be controlled too. For example, jamming DoS attacks on CAMs may endanger vehicular safety, and first and foremost are to be detected in real time. Our first goal is to discuss key literature on jamming modeling in VANETs and revisit some existing detection methods. Our second goal is to present and evaluate our own recent results on how to address the real-time jamming detection problem in V2X safety-critical scenarios with the use of AI. We conclude that our hybrid jamming detector, which combines statistical network traffic analysis with data mining methods, allows the achievement of acceptable performance even when random jitter accompanies the generation of CAMs, which complicates the analysis of the reasons for their losses in VANETs. The use case of the study is a challenging platooning C-ITS application, where V2X-enabled vehicles move together at highway speeds with short inter-vehicle gaps.

sted, utgiver, år, opplag, sider
IEEE, 2018. Vol. 32, nr 6, s. 15-21
Emneord [en]
Vehicle safety, Telecommunication traffic, Road traffic, Wireless communication, Networked control systems, Real-time systems, Vehicular ad hoc networks, Intelligent vehicles, Artificial intelligence, Cams, Jamming
HSV kategori
Forskningsprogram
Kommunikations- och beräkningssystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-71965DOI: 10.1109/MNET.2018.1800074ISI: 000451962400004Scopus ID: 2-s2.0-85057959135OAI: oai:DiVA.org:ltu-71965DiVA, id: diva2:1269010
Merknad

Validerad;2019;Nivå 2;2019-01-09 (marisr)

Tilgjengelig fra: 2018-12-07 Laget: 2018-12-07 Sist oppdatert: 2019-01-09bibliografisk kontrollert

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