MDFD: A multi-source data fusion detection framework for Sybil attack detection in VANETs

Chen, Ye; Lai, Yingxu; Zhang, Zhaoyi; Li, Hanmei; Wang, Yuhang

doi:10.1016/j.comnet.2023.109608

Cited by 18 publications

(4 citation statements)

References 34 publications

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“…Finally, defending VANET from DDoS attackers is a challenging one. 20 The introduced method can effectively detect the DDoS attack in SDN-based VANET for averting DDoS attacks. The evaluations show that the introduced method provides an effective solution for averting DDoS attacks.…”

Section: Intoductionmentioning

confidence: 99%

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Residual based temporal attention convolutional neural network for detection of distributed denial of service attacks in software defined network integrated vehicular adhoc network

Karthik,

Lakshmi,

Abraham

et al. 2023

Int J Network Mgmt

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Software defined network (SDN) integrated vehicular ad hoc network (VANET) is a magnificent technique for smart transportation as it raises the efficiency, safety, manageability, and comfort of traffic. SDN‐integrated VANET (SDN‐int‐VANET) has numerous benefits, but it is susceptible to threats like distributed denial of service (DDoS). Several methods were suggested for DDoS attack detection (AD), but the existing approaches to optimization have given a base for enhancing the parameters. An incorrect selection of parameters results in a poor performance and poor fit to the data. To overcome these issues, residual‐based temporal attention red fox‐convolutional neural network (RTARF‐CNN) for detecting DDoS attacks in SDN‐int‐VANET is introduced in this manuscript. The input data is taken from the SDN DDoS attack dataset. For restoring redundancy and missing value, developed random forest and local least squares (DRFLLS) are applied. Then the important features are selected from the pre‐processed data with the help of stacked contractive autoencoders (St‐CAE), which reduces the processing time of the introduced method. The selected features are classified by residual‐based temporal attention‐convolutional neural network (RTA‐CNN). The weight parameter of RTA‐CNN is optimized with the help of red fox optimization (RFO) for better classification. The introduced method is implemented in the PYTHON platform. The RTARF‐CNN attains 99.8% accuracy, 99.5% sensitivity, 99.80% precision, and 99.8% specificity. The effectiveness of the introduced technique is compared with the existing approaches.

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Section: Intoductionmentioning

confidence: 99%

“…However, there are numerous hazard vectors in both techniques, but common one is represented as DoS and DDoS attacks. Finally, defending VANET from DDoS attackers is a challenging one 20 …”

Section: Intoductionmentioning

confidence: 99%

Residual based temporal attention convolutional neural network for detection of distributed denial of service attacks in software defined network integrated vehicular adhoc network

Karthik,

Lakshmi,

Abraham

et al. 2023

Int J Network Mgmt

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“…In their scholarly article [68], the authors present a comprehensive analysis of the nature of Sybil attacks, utilizing trafc fow state data from multiple sources. Additionally, they propose a novel framework for detecting such attacks, known as the multi-source data combination detection (MDFD) method.…”

Section: Mdfdmentioning

confidence: 99%

Investigation of Different Mechanisms to Detect Misbehaving Nodes in Vehicle Ad-Hoc Networks (VANETs)

Nobahari,

Bakhshayeshi Avval,

Akhbari

et al. 2023

Security and Communication Networks

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The vehicle ad-hoc network (VANET) is a crucial technology that will play a significant role in shaping the future of transition systems, which is widely used as a subset of ad-hoc networks. VANET aims to ensure driver safety by establishing independent communication with nearby vehicles. A key requirement for successful data transmission is cooperation among nodes, as factors such as high mobility, limited radio range, signal fading, and noise lead to packet loss. Security issues in vehicle ad-hoc networks have recently become a major concern. One factor that affects security is the presence of abusive nodes in the network. Like selfish nodes, they are reluctant to share their sources with their neighbors and try to keep their property. The misbehavior of malicious nodes includes dissemination of false traffic information, false location information, and redirection of packets to a wrong path, retransmission of packets, impersonation, so these nodes should be tracked down to ensure the operation of the network. This article provides a complete summary of various research works proposed to detect selfish and malicious nodes and isolate them from honest vehicles. This review article first describes the types of attacks. It then presents the methods proposed by researchers to deal with uncooperative nodes and compares their performance based on parameters such as the number of misbehaving nodes detected, overhead, throughput, layer involved in the attacks.

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“…However, when the distance between vehicles is relatively small, RSSI sequences received from normal and malicious nodes show high similarity, and it is difficult to identify malicious nodes from normal nodes. To address the single detection factor limitation, Chen et al proposed a multi-scale data fusion detection framework for the Sybil attack [27]. By acquiring BSM, map data, and sensor data, the detection of malicious vehicles is accomplished using machine learning classification models.…”

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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MDFD: A multi-source data fusion detection framework for Sybil attack detection in VANETs

Cited by 18 publications

References 34 publications

Residual based temporal attention convolutional neural network for detection of distributed denial of service attacks in software defined network integrated vehicular adhoc network

Residual based temporal attention convolutional neural network for detection of distributed denial of service attacks in software defined network integrated vehicular adhoc network

Investigation of Different Mechanisms to Detect Misbehaving Nodes in Vehicle Ad-Hoc Networks (VANETs)

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

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