Misbehavior Detection in C-ITS: A comparative approach of local detection mechanisms

Kamel, Joseph; Jemaa, Inès Ben; Kaiser, Arnaud; Cantat, Loic; Urien, Pascal

doi:10.1109/vnc48660.2019.9062831

Cited by 19 publications

(9 citation statements)

References 20 publications

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“…Their proposal on vehicle trust model requires a central Trust Authority (TA) as well as a local vehicle trust module to combine different assessments. Some other studies were inspired by the public dataset VeReMi and simulated their own scenarios to create new attack types for their approaches [28]- [30]. They also generated several other features (e.g.…”

Section: B Ml-based Detection Mechanisms In Vanetsmentioning

confidence: 99%

Misbehavior Detection for Position Falsification Attacks in VANETs Using Machine Learning

2022

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Cooperative Intelligent Transport Systems (C-ITS) is an advanced technology for road safety and traffic efficiency over Vehicular Ad Hoc Networks (VANETs) allowing vehicles to communicate with other vehicles or infrastructures. The security of VANETs is one of the main concerns in C-ITS because there may be some attacks in such type of network that may endanger the safety of the passengers. Intrusion Detection Systems (IDS) play an important role to protect the vehicular network by detecting misbehaving vehicles. In general, the works in the literature use the same well-known features in a centralized IDS. In this paper, we propose a Machine Learning (ML) mechanism that takes advantage of three new features, which are mainly related to the sender position, allowing to enhance the performances of IDS for position falsification attacks. Besides, it presents a comparison of two different ML methods for classification, i.e. k-Nearest Neighbor (kNN) and Random Forest (RF) that are used to detect malicious vehicles using these features. Finally, Ensemble Learning (EL) which combines different ML methods, in our case kNN and RF, is also carried out to improve the detection performance. An IDS is constructed allowing vehicles to detect misbehavior in a distributed way, while the detection mechanism is trained centrally. The results demonstrate that the proposed mechanism gives better results, in terms of classification performance indicators and computational time, than the best previous approaches on average.INDEX TERMS misbehavior detection, machine learning, vehicular ad hoc network, intelligent transport systems, dataset.

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Section: B Ml-based Detection Mechanisms In Vanetsmentioning

confidence: 99%

Misbehavior Detection for Position Falsification Attacks in VANETs Using Machine Learning

2022

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“…The consistency checks verify if two attributes are consistent or if a received attribute value is consistent with the previous ones. We use the same misbehavior detectors specified in [4]. In this paper, we are more focused on addressing advanced attacks which create conflict situations.…”

Section: B Trust Assessment Frameworkmentioning

confidence: 99%

“…So far, several works exist on Misbehavior detection in the literature [4] [5]. However, most of them are mainly addressing manipulation attacks of the kinematic senders' data.…”

Section: Introductionmentioning

confidence: 99%

Trust Management Framework for Misbehavior Detection in Collective Perception Services

Zhang

Ben-Jemaa

Nashashibi

2022

2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV)

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Collective Perception Messages (CPM) enable vehicles to share their perceived objects with their neighbors in V2X network. These perception data extend local vehicles' perception and consequently improve road safety awareness. However, attacks on perception data are challenging and require advanced and efficient misbehavior detection mechanism especially in specific road scenarios where contradictory information need to be analysed. In this work, we introduce a trust management framework to detect misbehaving nodes through transmitted CPM messages. Our framework is based on trust assessment built through several processing steps. It addresses conflict situation when contradictory data are received using the Subjective Logic mechanism. The results show that our solution is effective in detecting misbehaving nodes based on their attributed trust scores. In addition, we show the impact of our solution and some CPM configuration parameters on safety services and especially on risk anticipation in intersection scenarios.

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“…One of the key results of SCA project is the F2MD simulator implemented on VEINS [22], [23] which provides local misbehaviour detection on received CAM and was used to assess multiple, configurable basic plausibility checks on CAM mobility data (such as location, speed, heading …) and to compare detection efficiency and performance of multiple intelligent local detection applications using fixed algorithms and others method based on artificial intelligence [24], [25].…”

Section: Misbehavior Detectionmentioning

confidence: 99%

Experimentation and Assessment of Pseudonym Certificate Management and Misbehavior Detection in C-ITS

Haidar

Makassikis²,

Sall

et al. 2021

IEEE Open J. Intell. Transp. Syst.

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In Cooperative Intelligent Transportation Systems (C-ITS), vehicles can communicate with one another and with the infrastructure. The openness of the transportation system to new functionalities and use cases may create new vulnerabilities that must be taken into account. In this paper, we study three main use cases, the effectiveness and performance evaluation of certificate reloading in real environment (while driving), the privacy evaluation of pseudonym certificates change strategies by proposing an attacker model on real exchanged messages, and finally, we implement Sybil and data replay attacks to test our misbehavior detection solution.

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Misbehavior Detection in C-ITS: A comparative approach of local detection mechanisms

Cited by 19 publications

References 20 publications

Misbehavior Detection for Position Falsification Attacks in VANETs Using Machine Learning

Misbehavior Detection for Position Falsification Attacks in VANETs Using Machine Learning

Trust Management Framework for Misbehavior Detection in Collective Perception Services

Experimentation and Assessment of Pseudonym Certificate Management and Misbehavior Detection in C-ITS

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