A Novel Mechanism for Misbehavior Detection in Vehicular Networks

Valentini, Edivaldo Pastori; Filho, Geraldo P. Rocha; Grande, Robson E. De; Ranieri, Caetano Mazzoni; Pereira, Lourenço Alves; Meneguette, Rodolfo I.

doi:10.1109/access.2023.3292055

Cited by 6 publications

(3 citation statements)

References 57 publications

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“…Future research should focus on enhancing the accuracy and adaptability of these algorithms, evaluating their performance in real-world scenarios, and developing hybrid approaches that combine unsupervised and supervised learning techniques. Detection of Anomalous Behavior in Smart Conveyance Operations (DAMASCO) is a security system described in Reference [ 145 ], which discusses the security challenges in VANETs and presents a system called DAMASCO. The system detects anomalies in vehicle-to-vehicle (V2V) communication using a statistical approach.…”

Section: Solutions For Security and Trust In Iovmentioning

confidence: 99%

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

Alalwany,

Mahgoub

2024

Sensors

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The Internet of Vehicles (IoV) is a technology that is connected to the public internet and is a subnetwork of the Internet of Things (IoT) in which vehicles with sensors are connected to a mobile and wireless network. Numerous vehicles, users, things, and networks allow nodes to communicate information with their surroundings via various communication channels. IoV aims to enhance the comfort of driving, improve energy management, secure data transmission, and prevent road accidents. Despite IoV’s advantages, it comes with its own set of challenges, particularly in the highly important aspects of security and trust. Trust management is one of the potential security mechanisms aimed at increasing reliability in IoV environments. Protecting IoV environments from diverse attacks poses significant challenges, prompting researchers to explore various technologies for security solutions and trust evaluation methods. Traditional approaches have been employed, but innovative solutions are imperative. Amid these challenges, machine learning (ML) has emerged as a potent solution, leveraging its remarkable advancements to effectively address IoV’s security and trust concerns. ML can potentially be utilized as a powerful technology to address security and trust issues in IoV environments. In this survey, we delve into an overview of IoV and trust management, discussing security requirements, challenges, and attacks. Additionally, we introduce a classification scheme for ML techniques and survey ML-based security and trust management schemes. This research provides an overview for understanding IoV and the potential of ML in improving its security framework. Additionally, it provides insights into the future of trust and security enhancement.

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Section: Solutions For Security and Trust In Iovmentioning

confidence: 99%

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

Alalwany,

Mahgoub

2024

Sensors

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“…The Detection of Anomalous Behavior in Smart Conveyance Operations (DAMASCO) system combines software and hardware solutions to detect DDoS attacks in VANETs, validated through simulations [109]. This system is particularly effective in ITS, where ensuring continuous network service is crucial for safety and efficiency.…”

Section: Dos and Ddos Attacks In V2x Communication Scenariosmentioning

confidence: 99%

Safeguarding the V2X Pathways: Exploring the Cybersecurity Landscape Through Systematic Review

Herman Muraro Gularte,

Alfredo Ruiz Vargas,

Paulo Javidi da Costa

et al. 2024

IEEE Access

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Vehicle-to-Everything (V2X) communication, essential for enhancing road safety, driving efficiency, and traffic management, must be robust against cybersecurity threats for successful deployment and acceptance. This survey comprehensively explores V2X security challenges, focusing on prevalent cybersecurity threats such as jamming, spoofing, Distributed Denial of Service (DDoS), and eavesdropping attacks. These threats were selected due to their prevalence and ability to compromise the integrity and reliability of V2X systems. Jamming can disrupt communications, spoofing can lead to data and identity manipulation, DDoS attacks can saturate system resources, and eavesdropping can compromise user privacy and information confidentiality. Addressing these major threats ensures that V2X systems are robust and secure for successful deployment and widespread acceptance. An extensive review uncovered a global landscape of V2X cybersecurity research. We highlight contributions from China in scientific publications and the United States in patent innovations, with notable advancements from leading corporations such as Qualcomm, LG, Huawei, Intel, Apple, and Samsung. This work educates and informs on the current state of V2X cybersecurity and identifies emerging trends and future research directions based on a year-byyear analysis of the literature and patents. The findings underscore the evolving cybersecurity landscape in V2X systems and the importance of continued innovation and research in this critical field. The survey navigates the complexities of securing V2X communications, emphasizing the necessity for advanced security protocols and technologies, and highlights innovative approaches within the global scientific and patent research context. By providing a panoramic view of the field, this survey sets the stage for future advancements in V2X cybersecurity INDEX TERMS Vehicle-to-everything, connected and automated vehicle, intellectual property in V2X cybersecurity, cyberattacks and attacks, V2X security.

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“…When it comes to security anomalies, DAMASCO [20] is a security-based system that aims to detect DoS attacks. A statistical approach was used to detect anomalies in vehicular communications where the MAC layer was addressed and assessed to identify potentially malicious nodes by the number of sent packets and block their activity.…”

Section: Anomaly Detectionmentioning

confidence: 99%

Road-Side Unit Anomaly Detection

Benzagouta,

Aniss,

Fouchal

et al. 2023

Vehicles

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Actors of the Cooperative Intelligent Transport Systems (C-ITS) generate various amounts of data. Useful information on various issues such as anomalies, failures, road profiles, etc., could be revealed from the analysis of these data. The analysis, could be managed by operators and vehicles, and its output could be very helpful for future decision making. In this study, we collected real data extracted from road operators. We analyzed these streams in order to verify whether abnormal behaviors could be observed in the data. Our main target was a very sensitive C-ITS failure, which is when a road-side unit (RSU) experiences transmission failure. The detection of such failure is to be achieved by end users (vehicles), which in turn would inform road operators which would then recover the failure. The data we analyzed were collected from various roads in Europe (France, Germany, and Italy) with the aim of studying the RSUs’ behavior. Our mechanism offers compelling results regarding the early detection of RSU failures. We also proposed a new C-ITS message dedicated to raise alerts to road operators when required.

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A Novel Mechanism for Misbehavior Detection in Vehicular Networks

Cited by 6 publications

References 57 publications

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

Safeguarding the V2X Pathways: Exploring the Cybersecurity Landscape Through Systematic Review

Road-Side Unit Anomaly Detection

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