CaTch: A Confidence Range Tolerant Misbehavior Detection Approach

Kamel, Joseph; Kaiser, Arnaud; Jemaa, Inès Ben; Cincilla, Pierpaolo; Urien, Pascal

doi:10.1109/wcnc.2019.8885740

Cited by 24 publications

(13 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The legacy version is much faster to compute the plausibility checks and returns a binary output to show whether a certain aspect of the message is plausible or not. The ET version is generally slower to compute the plausibility checks but returns an uncertainty factor that reflects the scale of the message implausibility [21].…”

Section: Local Detectionmentioning

confidence: 99%

Simulation Framework for Misbehavior Detection in Vehicular Networks

Kamel

Ansari

Petit

et al. 2020

IEEE Trans. Veh. Technol.

Self Cite

View full text Add to dashboard Cite

Cooperative Intelligent Transport Systems (C-ITS) is an ongoing technology that will change our driving experience in the near future. In such systems, vehicles and RoadSide Unit (RSU) cooperate by broadcasting V2X messages over the vehicular network. Safety applications use these data to detect and avoid dangerous situations on time. MisBehavior Detection (MBD) in C-ITS is an active research topic which consists of monitoring data semantics of the exchanged Vehicle-to-X communication (V2X) messages to detect and identify potential misbehaving entities. The detection process consists of performing plausibility and consistency checks on the received V2X messages. If an anomaly is detected, the entity may report it by sending a Misbehavior Report (MBR) to the Misbehavior Authority (MA). The MA will then investigate the event and decide to revoke the sender or not. In this paper, we present a MisBehavior Detection (MBD) simulation framework that enables the research community to develop, test, and compare MBD algorithms. We also demonstrate its capabilities by running example scenarios and discuss their results. Framework For Misbehavior Detection (F 2 MD) is open source and available for free on our github.

show abstract

Section: Local Detectionmentioning

confidence: 99%

Simulation Framework for Misbehavior Detection in Vehicular Networks

Kamel

Ansari

Petit

et al. 2020

IEEE Trans. Veh. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…These checks are simple and fast to calculate plausibility detectors. The features of the detection process in detailed in the following study [14]. Based on these detectors, the vehicle decides if a report should be send or not.…”

Section: Local Detectorsmentioning

confidence: 99%

Towards a Reliable Machine Learning-Based Global Misbehavior Detection in C–ITS: Model Evaluation Approach

Mahmoudi¹,

Kamel²,

Ben-Jemaa³

et al. 2020

Advances in Intelligent Systems and Computing

Self Cite

View full text Add to dashboard Cite

Global misbehavior detection in Cooperative Intelligent Transport Systems (C-ITS) is carried out by a central entity named Misbehavior Authority (MA). The detection is based on local misbehavior detection information sent by Vehicle's On-Board Units (OBUs) and by RoadSide Units (RSUs) called Misbehavior Reports (MBRs) to the MA. By analyzing these Misbehavior Reports (MBRs), the MA is able to compute various misbehavior detection information. In this work, we propose and evaluate different Machine Learning (ML) based solutions for the internal detection process of the MA. We show through extensive simulation and several detection metrics the ability of solutions to precisely identify different misbehavior types.

show abstract

“…In this work, we aggregated and implemented the checks used in multiple local detection works [8]. However, the implemented checks does not return a binary value, instead a plausibility factor is calculated as described in our previous work [9]. For more details on the implementation of the detectors, all the implementations are open-source on github [10].…”

Section: B Local Detection Checksmentioning

confidence: 99%

“…Table I shows the Evaluation Results of the AI Linkage Mechanism. The evaluation metrics are detailed in our previous publication [9]. As this system is replacing the LA, a high confidence in a perceived linkage is needed before it is considered.…”

Section: Simulation Settings and Scenariosmentioning

confidence: 99%

A Misbehavior Authority System for Sybil Attack Detection in C-ITS

Kamel

Haidar

Jemaa

et al. 2019

2019 IEEE 10th Annual Ubiquitous Computing, Electronics &Amp; Mobile Communication Conference (UEMCON)

Self Cite

View full text Add to dashboard Cite

Global misbehavior detection is an important backend mechanism in Cooperative Intelligent Transport Systems (C-ITS). It is based on the local misbehavior detection information sent by Vehicle's On-Board Units (OBUs) and by RoadSide Units (RSUs) called Misbehavior Reports (MBRs) to the Misbehavior Authority (MA). By analyzing these reports, the MA provides more accurate and robust misbehavior detection results. Sybil attacks pose a significant threat to the C-ITS systems. Their detection and identification may be inaccurate and confusing. In this work, we propose a Machine Learning (ML) based solution for the internal detection process of the MA. We show through extensive simulation that our solution is able to precisely identify the type of the Sybil attack and provide promising detection accuracy results.

show abstract

CaTch: A Confidence Range Tolerant Misbehavior Detection Approach

Cited by 24 publications

References 11 publications

Simulation Framework for Misbehavior Detection in Vehicular Networks

Simulation Framework for Misbehavior Detection in Vehicular Networks

Towards a Reliable Machine Learning-Based Global Misbehavior Detection in C–ITS: Model Evaluation Approach

A Misbehavior Authority System for Sybil Attack Detection in C-ITS

Contact Info

Product

Resources

About