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

Mahmoudi, Issam; Kamel, Joseph; Ben-Jemaa, Ines; Kaiser, Arnaud; Urien, Pascal

doi:10.1007/978-981-15-3750-9_6

Cited by 13 publications

(4 citation statements)

References 18 publications

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“…It should be noted that the primary objectives of the proposed system are to process video footage for recognizing risks with the predicted road-user trajectories in real time and to alleviate the data flow issues in conventional systems that handle road-related risks and phenomena [30,31]. However, when dealing with video data, characterized by its substantial file sizes and resource-intensive processing demands, transmitting data and retrieving information can be time-consuming.…”

Section: Results Of the Time Effectiveness Of The Proposed System And...mentioning

confidence: 99%

NAVIBox: Real-Time Vehicle–Pedestrian Risk Prediction System in an Edge Vision Environment

Lee,

Cho,

Noh

et al. 2023

Electronics

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This study introduces a novel system, termed NAVIBox, designed to proactively identify vehicle–pedestrian risks using vision sensors deployed within edge computing devices in the field. NAVIBox consolidates all operational components into a single unit, resembling an intelligent CCTV system, and is built upon four core pipelines: motioned-video capture, object detection and tracking, trajectory refinement, and predictive risk recognition and warning decision. The operation begins with the capture of motioned video through a frame difference approach. Road users are subsequently detected, and their trajectories are determined using a deep learning-based lightweight object detection model, in conjunction with the Centroid tracker. In the trajectory refinement stage, the system converts the perspective of the original image into a top view and conducts grid segmentation to capture road users’ behaviors precisely. Lastly, vehicle–pedestrian risks are predicted by analyzing these extracted behaviors, and alert signals are promptly dispatched to drivers and pedestrians when risks are anticipated. The feasibility and practicality of the proposed system have been verified through implementation and testing in real-world test sites within Sejong City, South Korea. This systematic approach presents a comprehensive solution to proactively identify and address vehicle–pedestrian risks, enhancing safety and efficiency in urban environments.

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Section: Results Of the Time Effectiveness Of The Proposed System And...mentioning

confidence: 99%

NAVIBox: Real-Time Vehicle–Pedestrian Risk Prediction System in an Edge Vision Environment

Lee,

Cho,

Noh

et al. 2023

Electronics

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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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“…Four features were considered to detect the attack geographical position validation: acceptance field verification, speed variation verification, and received signal strength. Issam Mahmoudi et al [6] proposed a ML-based global misbehavior detection system to analyze the reported misbehavior sent by vehicles and Roadside Units (RSU). A set of algorithms was trained to assess the detection based on a few selected features: (i) plausibility and consistency check features, (ii) communication kinematic data features, and (iii) generic features.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, vehicular networks have benefited from the advances in machine learning in the areas of network security. Indeed, several ML-based Misbehavior Detection Systems (ML-based MDSs) have been proposed for the efficient detection of false position attacks [5][6][7][8][9][10]. However, existing solutions leverage numerous features which increase the computational complexity and overhead.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Misbehavior Detection System for Detecting False Position Attacks in Vehicular Networks

Hawlader

Boualouache

Faye

et al. 2021

2021 IEEE International Conference on Communications Workshops (ICC Workshops)

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Position falsification attacks are one of the most dangerous internal attacks in vehicular networks. Several Machine Learning-based Misbehavior Detection Systems (ML-based MDSs) have recently been proposed to detect these attacks and mitigate their impact. However, existing ML-based MDSs require numerous features, which increases the computational time needed to detect attacks. In this context, this paper introduces a novel ML-based MDS for the early detection of position falsification attacks. Based only on received positions, our system provides real-time and accurate predictions. Our system is intensively trained and tested using a publicly available data set, while its validation is done by simulation. Six conventional classification algorithms are applied to estimate and construct the best model based on supervised learning. The results show that the proposed system can detect position falsification attacks with almost 100% accuracy.

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

Cited by 13 publications

References 18 publications

NAVIBox: Real-Time Vehicle–Pedestrian Risk Prediction System in an Edge Vision Environment

NAVIBox: Real-Time Vehicle–Pedestrian Risk Prediction System in an Edge Vision Environment

Misbehavior Detection for Position Falsification Attacks in VANETs Using Machine Learning

Intelligent Misbehavior Detection System for Detecting False Position Attacks in Vehicular Networks

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