The Impact of Sybil Attacks on Vehicular Fog Networks

Benadla, Sarra; Boudia, Omar Rafik Merad

doi:10.1109/icrami52622.2021.9585965

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…The location information in the vehicular network must be validated using different devices such as radar, GPS, etc. The algorithm eliminates the improbable spots if the vehicles do not have such equipment [84]. The tamper-proof GPS device is one of the most effective solutions for safeguarding vehicle position.…”

Section: F Attacks In Vfcmentioning

confidence: 99%

Toward Secure and Trustworthy Vehicular Fog Computing: A Survey

Nazih,

Benamar,

Lamaazi

et al. 2024

IEEE Access

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The integration of fog computing in vehicular networks has led to significant advancements in road safety, traffic control, entertainment, and comfort services. Vehicular Fog Computing (VFC) emerges as an optimistic solution, offering a pathway to responsive service requests. VFC uses either onboard vehicle computers or vehicles as fog infrastructure between the underlying networks and the cloud, addressing the limitations of centralized data processing in traditional cloud computing. However, VFC faces security vulnerabilities due to the open nature of its deployment. In this survey, we explore the security threats confronting VFC and review existing solutions related to their detection and mitigation capabilities. This paper provides a comprehensive overview of the foundational concept of Fog Computing, VFC architectures, and their critical role in various intelligent computing applications. Moreover, it spotlights the trust and security concerns in deploying VFC and real-time big data analytics within the vehicular environment. This survey identifies pressing issues and outlines potential research directions, offering insights for the research community to address while designing secure VFC architectures.

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Section: F Attacks In Vfcmentioning

confidence: 99%

Toward Secure and Trustworthy Vehicular Fog Computing: A Survey

Nazih,

Benamar,

Lamaazi

et al. 2024

IEEE Access

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“…The following attacks can track a vehicle’s broadcast messages. The Sybil attack [ 59 ] undermines the system by creating many fictitious nodes, while spoofing [ 60 ] manipulates communication by impersonating another device. MiTM attacks [ 56 ] secretly relay and possibly alter the communication between two parties who believe they are directly communicating with each other.…”

Section: Vehicular Network: Backgroundmentioning

confidence: 99%

Investigation of Security Threat Datasets for Intra- and Inter-Vehicular Environments

Haddaji,

Ayed,

Chaari Fourati

et al. 2024

Sensors

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Vehicular networks have become a critical component of modern transportation systems by facilitating communication between vehicles and infrastructure. Nonetheless, the security of such networks remains a significant concern, given the potential risks associated with cyberattacks. For this purpose, artificial intelligence approaches have been explored to enhance the security of vehicular networks. Using artificial intelligence algorithms to analyze large datasets can enable the early identification and mitigation of potential threats. However, developing and testing effective artificial-intelligence-based solutions for vehicular networks necessitates access to diverse datasets that accurately capture the various security challenges and attack scenarios in this context. In light of this, the present survey comprehensively examines the vehicular network environment, the associated security issues, and existing datasets. Specifically, we begin with a general overview of the vehicular network environment and its security challenges. Following this, we introduce an innovative taxonomy designed to classify datasets pertinent to vehicular network security and analyze key features of these datasets. The survey concludes with a tailored guide aimed at researchers in the vehicular network domain. This guide offers strategic advice on selecting the most appropriate datasets for specific research scenarios in the field.

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“…In order to deal with the potential threat of Sybil attacks in CAV networks, we must conduct more in-depth research on attack detection and countermeasure strategies. Benadla et al have provided a brief description of the impact of Sybil attacks on vehicular networks and a detailed categorization of Sybil attack detection methods in VANETs [13]. These studies provide valuable references but still need to be further explored in depth in order to establish a more complete and effective defense mechanism.…”

Section: Introductionmentioning

confidence: 99%

Sybil Attacks Detection and Traceability Mechanism Based on Beacon Packets in Connected Automobile Vehicles

Zhu,

Zeng,

Weng

et al. 2024

Sensors

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Connected Automobile Vehicles (CAVs) enable cooperative driving and traffic management by sharing traffic information between them and other vehicles and infrastructures. However, malicious vehicles create Sybil vehicles by forging multiple identities and sharing false location information with CAVs, misleading their decisions and behaviors. The existing work on defending against Sybil attacks has almost exclusively focused on detecting Sybil vehicles, ignoring the traceability of malicious vehicles. As a result, they cannot fundamentally alleviate Sybil attacks. In this work, we focus on tracking the attack source of malicious vehicles by using a novel detection mechanism that relies on vehicle broadcast beacon packets. Firstly, the roadside units (RSUs) randomly instruct vehicles to perform customized key broadcasting and listening within communication range. This allows the vehicle to prove its physical presence by broadcasting. Then, RSU analyzes the beacon packets listened to by the vehicle and constructs a neighbor graph between the vehicles based on the customized particular fields in the beacon packets. Finally, the vehicle’s credibility is determined by calculating the edge success probability of vehicles in the neighbor graph, ultimately achieving the detection of Sybil vehicles and tracing malicious vehicles. The experimental results demonstrate that our scheme achieves the real-time detection and tracking of Sybil vehicles, with precision and recall rates of 98.53% and 95.93%, respectively, solving the challenge of existing detection schemes failing to combat Sybil attacks from the root.

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The Impact of Sybil Attacks on Vehicular Fog Networks

Cited by 9 publications

References 29 publications

Toward Secure and Trustworthy Vehicular Fog Computing: A Survey

Toward Secure and Trustworthy Vehicular Fog Computing: A Survey

Investigation of Security Threat Datasets for Intra- and Inter-Vehicular Environments

Sybil Attacks Detection and Traceability Mechanism Based on Beacon Packets in Connected Automobile Vehicles

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