Misbehavior detection and efficient revocation within VANET

Hasrouny, Hamssa; Samhat, Abed Ellatif; Bassil, Carole; Laouiti, Anis

doi:10.1016/j.jisa.2019.03.001

Cited by 22 publications

(8 citation statements)

References 76 publications

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“…Trust-centric models are based on voting or scoring schemes in which the reliability of a node broadcasting a message is voted on by the other nodes receiving the message. Once the cumulative voting score exceeds a level against the node, it is declared as an intruder, and its message dissemination is blocked [ 17 , 18 , 19 ]. A data-centric approach evaluates the driver’s behavior with respect to shared messages.…”

Section: Related Workmentioning

confidence: 99%

RSU-Based Online Intrusion Detection and Mitigation for VANET

Haydari

Yılmaz

2022

Sensors

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Secure vehicular communication is a critical factor for secure traffic management. Effective security in intelligent transportation systems (ITS) requires effective and timely intrusion detection systems (IDS). In this paper, we consider false data injection attacks and distributed denial-of-service (DDoS) attacks, especially the stealthy DDoS attacks, targeting integrity and availability, respectively, in vehicular ad-hoc networks (VANET). Novel machine learning techniques for intrusion detection and mitigation based on centralized communications through roadside units (RSU) are proposed for the considered attacks. The performance of the proposed methods is evaluated using a traffic simulator and a real traffic dataset. Comparisons with the state-of-the-art solutions clearly demonstrate the superior detection and localization performance of the proposed methods by 78% in the best case and 27% in the worst case, while achieving the same level of false alarm probability.

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Section: Related Workmentioning

confidence: 99%

RSU-Based Online Intrusion Detection and Mitigation for VANET

Haydari

Yılmaz

2022

Sensors

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show abstract

“…Trust-centric models are based on voting or scoring schemes, in which the reliability of a node broadcasting a message is voted by the other nodes receiving the message. Once the cumulative voting score exceeds a level against the node, it is declared as intruder, and its message dissemination is blocked [15], [16]. General data-centric approach evaluates the driver's behavior with respect to shared messages.…”

Section: A False Data Injection Attacksmentioning

confidence: 99%

RSU-Based Online Intrusion Detection and Mitigation for VANET

Haydari¹,

Yılmaz²

2022

Preprint

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Secure vehicular communication is a critical factor for secure traffic management. Effective security in intelligent transportation systems (ITS) requires effective and timely intrusion detection systems (IDS). In this paper, we consider false data injection attacks and distributed denial-of-service (DDoS) attacks, especially the stealthy DDoS attacks, targeting the integrity and availability, respectively, in vehicular ad-hoc networks (VANET). Novel statistical intrusion detection and mitigation techniques based on centralized communications through roadside units (RSU) are proposed for the considered attacks. The performance of the proposed methods are evaluated using a traffic simulator and a real traffic dataset. Comparisons with the state-of-the-art solutions clearly demonstrate the superior performance of the proposed methods in terms of quick and accurate detection and localization of cyberattacks.

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“…Many studies investigated the problem of misbehavior detection in VANET [7,9,[12][13][14][15][16][17][18]. These studies can be categorized based on the actors used to be validated into two approaches data-centric and entity-centric.…”

Section: Related Workmentioning

confidence: 99%

“…Many misbehaviors detection have been proposed for VANET in the literature [7,9,[11][12][13][14][15][16][17][18][19][20]. Most of these solutions are based on data-centric that classify the vehicles based on the quality of the cooperative awareness messages shared among neighboring vehicles.…”

Section: Introductionmentioning

confidence: 99%

An Improved Robust Misbehavior Detection Scheme for Vehicular Ad Hoc Network

et al. 2022

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Vehicular Ad Hoc Networks (VANETs) have emerged to improve road safety and traffic efficiency and provide passengers comfort. Most VANET applications rely on the cooperation among vehicles sharing their sensed information. However, misbehaving vehicles which send false information can disrupt the VANETs potential. Although many solutions have been proposed to defend against misbehaving vehicles in VANET, most of these solutions relays on honest majority assumptions and are thus vulnerable to collusion attacks. Colluding vehicles send fake information, and because detection depends on cooperation, such information misleads benign vehicles to make an accurate decision. This study proposes an improved Robust Misbehavior Detection Scheme (iRMDS) by replacing the statistics-based detection threshold, which assumes an honest majority, with a machine learning-based classifier. A Neuro-Kalman-Based Robust Misbehavior detection scheme is proposed in three phases. In the first phase, attackers-Independent features are extracted from signal properties such as the receive signal strength and signal direction and have been integrated with context information features. In the second phase, the Kalman filter algorithm has been designed to extract consistent patterns of context information for each vehicle. That is, the innovation errors of the Kalman filter have been utilized as the input features to train the misbehavior detection model. In the third phase, the artificial neural network algorithm is integrated with the outputs of the Kalman Filter algorithm to recognize the malicious pattern. Results show that the overall performance of the proposed iRMDS solution achieves a 3.44% improvement compared to the related work. Such enhancement is promising in realizing reliable VANET applications to improve road safety, traffic efficiency, and passenger comfort.

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Misbehavior detection and efficient revocation within VANET

Cited by 22 publications

References 76 publications

RSU-Based Online Intrusion Detection and Mitigation for VANET

RSU-Based Online Intrusion Detection and Mitigation for VANET

RSU-Based Online Intrusion Detection and Mitigation for VANET

An Improved Robust Misbehavior Detection Scheme for Vehicular Ad Hoc Network

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