A Robust Eco-Routing Protocol against Malicious Data in Vehicular Networks

Basaras, Pavlos; Μαγλαράς, Λέανδρος; Katsaros, Dimitrios; Janicke, Helge

doi:10.1109/wmnc.2015.27

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…as backbone to receive the real/predicted status of the traffic flow. In addition, the predicted data of the traffic flow offer advantages not only for the individual vehicles, but accuracy of these data will optimise also the performance of the other VANET applications such as, prediction that can be a basis for clustering and routing of packets (Maglaras and Katsaros, 2016), intrusion detection data in VANET networks (Maglaras and Katsaros, 2016); Basaras et al, 2015), prediction of the road risk and other accuracy data for mobile collaborative applications (Guetmi and Imine, 2017).…”

Section: Traffic Data Collectionmentioning

confidence: 99%

Vehicular-cloud simulation framework for predicting traffic flow data

Abdelatif

Derdour

Ahmim

et al. 2020

IJITST

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The traffic flow prediction has become an important process tailored with the exponential development of cities and the transportation systems. The main purpose of the prediction task is to improve the logistic services and reduce the cost of the road congestion. In this paper, we propose a vehicular-cloud simulation framework with a layer of traffic cloud services to predict accurate traffic flow data. Learning of supervised traffic flow data from several data sources is the core of these services. Particularly, we focus on a particular type of dependency (i.e., monotone dependency) between the learning traffic inputs and its responses. The learning algorithm we propose aims to solve the regression problem by predicting values of a continuous measure. The accuracy of the proposed cloud services have been tested under congestion conditions, where the results show better performances over short periods and daily forecasts.

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Section: Traffic Data Collectionmentioning

confidence: 99%

Vehicular-cloud simulation framework for predicting traffic flow data

Abdelatif

Derdour

Ahmim

et al. 2020

IJITST

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“…In this regard, both specification-based and anomaly-based treatments (Müter et al, 2010) have been investigated. Moreover, an attempt to deflect attacks using honeypots has been described in Verendel et al (2008), while other novel techniques for filtering out tweaked data have been recently developed (Basaras et al, 2015).…”

mentioning

confidence: 99%

Improved Security Performance for VANET Simulations

et al. 2016

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Upcoming deployments of Vehicular Ad Hoc Networks (VANETs) in Europe are expected to sign and verify packets secured by cryptographic signatures by default. Thus, when VANET simulations are used for development and test of applications building upon vehicular communication, the overhead induced by security extensions to the ITS-G5 protocol stack shall not be neglected. This paper presents a standard compliant simulation model capable to handle secured messages. Beside its suitability for Hardware-in-the-Loop simulations employing secured communication, the model's major advantage is the minimisation of the simulation environment's performance penalty linked with cryptographic computations.

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“…an attempt to deflect attacks using honeypots has been described in [162]. Finally, new techniques for filtering out tweaked data have been recently developed [28].…”

Section: Blocking the Outspread Of Undesired Data In Vehicular Networkmentioning

confidence: 99%

Spreading dynamics in complex networks with applications in vehicular networks

Basaras¹,

Μπασαράς²

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Η ανάλυση σύμπλεκτων (complex) δικτύων έχει λάβει τεράστια προσοχή τα τελευταία χρόνια, εξαιτίας της ανάπτυξης των online social δικτύων, και της ύπαρξης τεραστίου όγκου δεδομένων σχετικών με τεχνολογικά (Internet, Web, power grids), βιολογικά δίκτυα (αλληλεπιδράσεων πρωτεϊνών, γονιδίων, εξάπλωση ασθενειών), κ.τ.λ. Η παρούσα διατριβή εξερεύνησε το ζήτημα της διάδοσης “πληροφορίας” (φημών, ιών,…) σε σύμπλεκτα δίκτυα, καθώς και εφαρμογές των σχετικών αποτελεσμάτων σε ad hoc δίκτυα οχημάτων. Ειδικότερα, μελετήθηκε το πρόβλημα της μεγιστοποίησης διάδοσης (influence maximization), και αναπτύχθηκαν state-of-the-art αλγορίθμοι για την εύρεση των πιο ικανών διασκορπιστών πληροφορίας (influential spreaders) σε μονοεπίπεδα και πολυεπίπεδα σύμπλεκτα δίκτυα, καθώς και προσαρμογή αυτών σε πρωτόκολλα δρομολόγησης σε ad hoc δίκτυα οχημάτων (vehicular ad hoc networks). Επιπρόσθετα, μελετήθηκε το δυϊκό πρόβλημα της ελαχιστοποίησης διάδοσης “κακοήθους” (malicious) πληροφορίας σε σύμπλεκτα δίκτυα, για πρώτη φορά σε online fashion, και αναπτύχθηκε αλγόριθμος για την επιλογή των καλύτερων παρακωλυτών διάδοσης κακοήθους πληροφορίας. Κάτω από το πρίσμα των προσφάτων εξελίξεων στα self-driving cars, το προαναφερθέν πρόβλημα μελετήθηκε και για ad hoc δίκτυα οχημάτων, και αναπτύχθηκε μια τεχνική black-listing για όσα οχήματα έχουν περάσει στον έλεγχο κάποιου “adversary”. Τέλος, μελετήθηκε και γενικεύτηκε η ισχύς του “παραδόξου της φιλίας” για μια σειρά από τοπολογικά χαρακτηριστικά των κόμβων ενός δικτύου, και αναπτύχθηκαν εφαρμογές του για την μεγιστοποίηση (ελαχιστοποίηση) της διάδοσης της πληροφορίας στα σύμπλεκτα δίκτυα.

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A Robust Eco-Routing Protocol against Malicious Data in Vehicular Networks

Cited by 4 publications

References 22 publications

Vehicular-cloud simulation framework for predicting traffic flow data

Vehicular-cloud simulation framework for predicting traffic flow data

Improved Security Performance for VANET Simulations

Spreading dynamics in complex networks with applications in vehicular networks

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