A hybrid framework to prevent VANET from Sybil Attack

Faisal, Syed M.; Gupta, Bineet Kumar; Zaidi, Taskeen

doi:10.1109/impact55510.2022.10029103

Cited by 2 publications

(3 citation statements)

References 22 publications

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“…The model gave a good answer for both the different kinds of vehicles and the trust factor. The authors in [ 160 ] focused on identifying potential Sybil vehicles and protecting messages from Sybil attacks. The proposed strategy integrates metaheuristic methods during the establishment of communication to identify possible Sybil nodes and employs trust certification mechanisms to guarantee the integrity of messages.…”

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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“…Compared with the overhead of direct identity detection mechanisms, indirect identity detection mechanisms are more lightweight, such as the detection methods based on vehicle cooperation [18,21,[34][35][36]. Panchal [37] proposed a method of separating Sybil attacks using adjacent information in VANETs.…”

Section: Indirect Identity Detection Mechanismsmentioning

confidence: 99%

“…In addition, the collusion [15] and separation behaviors between malicious and Sybil vehicles make malicious vehicles exhibit similar behavioral characteristics as normal vehicles. This is the main reason why many mitigation solutions against Sybil attacks can only detect Sybil vehicles but cannot accurately track malicious vehicles [16][17][18][19][20][21]. Notably, malicious vehicles are the source of Sybil attacks.…”

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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A hybrid framework to prevent VANET from Sybil Attack

Cited by 2 publications

References 22 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

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

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