Detection method to eliminate Sybil attacks in Vehicular Ad-hoc Networks

Zhang, Zhaoyi; Lai, Yingxu; Chen, Ye; Wei, Jingwen; Wang, Yuhang

doi:10.1016/j.adhoc.2023.103092

Cited by 10 publications

(11 citation statements)

References 4 publications

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“…In addition, since power affects RSSI, this scheme can only detect malicious vehicles without power control but cannot detect malicious vehicles with transmission power control [40]. Zhang [41] proposed a detection method based on Basic Safety Message (BSM) packets in 2023, which mainly detects the detected vehicle by receiving the receiver (Recv) of the BSM message broadcast by the detected vehicle. The Recv calculates the transmission distance (T d ) of the BSM message according to the sending time and receiving time information of the BSM message.…”

Section: Indirect Identity Detection Mechanismsmentioning

confidence: 99%

“…In order to reduce the situation where normal vehicles cannot generally run due to detection errors, our goal is to increase the recall as much as possible while ensuring precision. Our comparative schemes are the Hunting scheme [17] and the Eliminate scheme [41]. The Hunting scheme [17] is chosen due to its graph-based detection mechanism.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Our work is based on the Hunting scheme [17] to trace malicious vehicles and has improved the detection rate of Sybil vehicles compared to this scheme. On the other hand, the Eliminate scheme [41] is chosen because although both this scheme and our proposed traceability mechanism are based on beacon packet analysis, this scheme utilizes the unique signal source of beacon packets to trace malicious vehicles. In contrast, our traceability mechanism traces malicious vehicles through the possibility of successful communication between Sybil vehicles and malicious vehicles.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…It also reveals that the Hunting scheme can maintain a relatively stable performance when dealing with different densities of Sybil vehicles. The point strategy of the Eliminate scheme [41] depends on the designation of neighboring vehicles. As the density of Sybil vehicles increases, the probability that neighboring vehicles will be mixed with Sybil vehicles increases.…”

Section: Increasing the Proportion Of Sybil Vehiclesmentioning

confidence: 99%

“…We use max(x,1) to treat the denominator non-0, so the calculated P m approaches 0 and P s&m approaches P s . In the Eliminate [41] scheme, which utilizes suspicious Sybil vehicles to locate the source of packets that are further sent as malicious vehicles, the detection rate of malicious vehicles remains stable, as the overall detection rate of Sybil vehicles remains stable. However, after detecting Sybil vehicles, our scheme leverages the conspiracy between Sybil vehicles and malicious vehicles to trace the malicious vehicles.…”

Section: Increasing the Proportion Of Sybil Vehiclesmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Indirect Identity Detection Mechanismsmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Increasing the Proportion Of Sybil Vehiclesmentioning

confidence: 99%

Section: Increasing the Proportion Of Sybil Vehiclesmentioning

confidence: 99%

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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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Developing an optimized routing protocol with rumor riding technique for detection of Sybil attack in VANET environment

Navinkumar,

Somasundaram

2024

Int J Communication

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SummaryAn attack on VANET known as a Sybil attack involves the attacker claiming or stealing multiple identities with the intent of causing havoc on the network's operation by disseminating false information. To protect the VANET network from Sybil attack, a variety of methods have been proposed. In order to compensate for the Sybil attack on VANETs, a rumor riding with salp swarm optimization (SSO) based Routing protocol is developed in this research work. The primary goal of this investigation is to discover how VANETs are vulnerable to the Sybil attack in order to improve their security. Rumor riding will be used to identify the Sybil attack in the suggested strategy. VANETs are protected by the Blowfish Algorithm, which uses SSO to select the best key for each network. The collective intelligence and dynamic adaptation of SALP swarm optimization algorithm enable efficient identification of rumor sources in complex networks. The algorithm is scalable, flexible, and adept at reducing false positives and negatives, making it suitable for large‐scale monitoring. Its parallel processing capabilities and robustness ensure rapid and reliable detection. Using the NS2 platform, the proposed method is compared to existing benchmark in terms of delay, packet delivery ratio (PDR), packet loss, throughput, and attack detection rate. The proposed SSO has reduced the packet loss by nearly 5% and delay by nearly 7%, when compared with existing techniques. The delivery ratio of packet for proposed model is increased up to 8% of PDR, when compared with ACO, PSO, and FA. According to this evidence, it is clear that the proposed SSO outperformed existing approaches in a variety of performance metrics.

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Detecting Sybil Attacks in VANET: Exploring Feature Diversity and Deep Learning Algorithms with Insights into Sybil Node Associations

Sultana,

Grover,

Tripathi

et al. 2024

J Netw Syst Manage

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Detection method to eliminate Sybil attacks in Vehicular Ad-hoc Networks

Cited by 10 publications

References 4 publications

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

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

Developing an optimized routing protocol with rumor riding technique for detection of Sybil attack in VANET environment

Detecting Sybil Attacks in VANET: Exploring Feature Diversity and Deep Learning Algorithms with Insights into Sybil Node Associations

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