Misbehavior Detection using Machine Learning in Vehicular Communication Networks

Gyawali, Sohan; Qian, Yi

doi:10.1109/icc.2019.8761300

Cited by 88 publications

(67 citation statements)

References 14 publications

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“…Moreover, the intrusion detection mechanism can detect and prevent inside attackers. Many works can be found in the literature for intrusion detection, and some recent works have shown that a high detection rate could achieve using machine learning based methods [132], [133]. However, the efficiency of these kinds of schemes could be further improved to fit V2X applications.…”

Section: G Intrusion Detection Mechanismmentioning

confidence: 99%

Recent Advances and Challenges in Security and Privacy for V2X Communications

Huang

Fang

Qian

et al. 2020

IEEE Open J. Veh. Technol.

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In recent studies, vehicular networks have been considered as a promising solution to achieve better traffic management and to improve the driving experience of drivers. In vehicular networks, vehicle-to-everything (V2X) services, e.g. onroad traffic information exchange and location-based services, are provided to facilitate road safety for vehicles and traffic management for the relevant authorities. Dedicated Short Range Communications is specifically designed for V2X communications, and recently the cellular network has shown great potential to support V2X with better performance and more applications. Due to the wireless nature of V2X communications, how to secure V2X communications and guarantee the privacy of users are great challenges that have hampered the implementation of vehicular services. Many solutions have been proposed by researchers in last two decades. In this paper, we present a comprehensive survey on the state-of-the-art solutions concerning security and privacy for V2X communications. For security, detailed discussions on cryptography based schemes and trust based schemes are provided. For privacy, we summarize and compare general solutions in preserving identity privacy and location privacy. Cellular based V2X communications have shown many advantages over DSRC, and the oncoming fifth-generation cellular technology is going to provide more possibilities to V2X. Thus, security architectures and solutions for cellular based communications are also illustrated and discussed. Finally, we summarize the remaining challenges and point out future research directions.

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Section: G Intrusion Detection Mechanismmentioning

confidence: 99%

Recent Advances and Challenges in Security and Privacy for V2X Communications

Huang

Fang

Qian

et al. 2020

IEEE Open J. Veh. Technol.

Self Cite

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“…[21][22][23][24][25] (3) Category 3: studies satisfying the filtering criteria for Category 2, but not including DDoS attacks. [26][27][28][29][30][31][32][33][34] (4) Category 4: studies that used ML techniques trained on datasets to evaluate detection of different attacks including DDoS, but not on a VANET environment architecture. [35][36][37][38][39][40] (5) Category 5: studies introducing frameworks for generating datasets for the VANET environment.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Many studies have presented different ML techniques trained on datasets for detecting different types of attacks; however, they have not included or considered DDoS attacks. [26][27][28][29][30][31][32][33][34] Ghaleb et al 26 used ANN model to detect malicious traffic in VANET. They trained ANN on a next generation simulation (NGSIM) dataset using MATLAB tools, and the results showed an accuracy of 99%.…”

Section: Ml-based Studies For Malicious Attacks On Vanetmentioning

confidence: 99%

A simulation work for generating a novel dataset to detect distributed denial of service attacks on Vehicular Ad hoc NETwork systems

Alhaidari

Alrehan

2021

International Journal of Distributed Sensor Networks

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Vehicular Ad hoc NETwork is a promising technology providing important facilities for modern transportation systems. It has garnered much interest from researchers studying the mitigation of attacks including distributed denial of service attacks. Machine learning techniques, which mainly rely on the quality of the datasets used, play a role in detecting many attacks with a high level of accuracy. We conducted a comprehensive literature review and found many limitations on the datasets available for distributed denial of service attacks on Vehicular Ad hoc NETwork including the following: unavailability of online versions, an absence of distributed denial of service traffic, unrepresentative of Vehicular Ad hoc NETwork, and no information regarding the network configurations. Therefore, in this article, we proposed a novel simulation technique to generate a valid dataset called Vehicular Ad hoc NETwork distributed denial of service dataset, which is dedicated to Vehicular Ad hoc NETworks. Vehicular Ad hoc NETwork distributed denial of service dataset holds information on distributed denial of service attack traffic considering Vehicular Ad hoc NETwork architecture, traffic density, attack intensity, and nodes mobility. Well-known simulation tools such as SUMO, OMNeT++, Veins, and INET were used to ensure that all the properties of Vehicular Ad hoc NETwork have been captured. We then compared Vehicular Ad hoc NETwork distributed denial of service dataset with several studies to prove its novelty and evaluated the dataset using several machine learning models. We confirmed that studied models using this dataset achieved high accuracy above 99.5% except support-vector machine that achieved 97.3%.

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“…Gyawali et al [4] also used the dataset for an ML application using a Feed Forward Neural Network (FFNN) and an SVM. They calculated their solution's detection metrics including the accuracy, precision, recall and F 1 Score.…”

Section: Related Workmentioning

confidence: 99%

VeReMi Extension: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs

Kamel

Wolf

Heijden

et al. 2020

ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

116

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Cooperative Intelligent Transport Systems (C-ITS) is a new upcoming technology that aims at increasing road safety and reducing traffic accidents. C-ITS is based on peer-to-peer messages sent on the Vehicular Ad hoc NETwork (VANET). VANET messages are currently authenticated using digital keys from valid certificates. However, the authenticity of a message is not a guarantee of its correctness. Consequently, a misbehavior detection system is needed to ensure the correct use of the system by the certified vehicles. Although a large number of studies are aimed at solving this problem, the results of these studies are still difficult to compare, reproduce and validate. This is due to the lack of a common reference dataset. For this reason, the original VeReMi dataset was created. It is the first public misbehavior detection dataset allowing anyone to reproduce and compare different results. VeReMi is used in a number of studies and is currently the only dataset in its field. In this Paper, we extend the dataset by adding realistic a sensor error model, a new set of attacks and larger number of data points. Finally, we also provide benchmark detection metrics using a set of local detectors and a simple misbehavior detection mechanism.

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Misbehavior Detection using Machine Learning in Vehicular Communication Networks

Cited by 88 publications

References 14 publications

Recent Advances and Challenges in Security and Privacy for V2X Communications

Recent Advances and Challenges in Security and Privacy for V2X Communications

A simulation work for generating a novel dataset to detect distributed denial of service attacks on Vehicular Ad hoc NETwork systems

VeReMi Extension: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs

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