RSU-Based Online Intrusion Detection and Mitigation for VANET

Haydari, Ammar; Yılmaz, Yasin

doi:10.3390/s22197612

Cited by 21 publications

(13 citation statements)

References 70 publications

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“…Training TD3: Using the legitimately collected data and the data from simulated wormhole attacks, the TD3 algorithm is trained. The objective is to discover the policy that maximizes the predicted cumulative reward while separating patterns of wormhole attack from normal behavior [17].…”

Section: Functions Of Td3 In Detecting Wormhole Attacks In Vanetmentioning

confidence: 99%

Enhancing VANET Security: Efficient Communication and Wormhole Attack Detection using VDTN Protocol and TD3 Algorithm

2024

KSII TIIS

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Due to the rapid evolution of vehicular ad hoc networks (VANETs), effective communication and security are now essential components in providing secure and reliable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. However, due to their dynamic nature and potential threats, VANETs need to have strong security mechanisms. This paper presents a novel approach to improve VANET security by combining the Vehicular Delay-Tolerant Network (VDTN) protocol with the Deep Reinforcement Learning (DRL) technique known as the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. A store-carry-forward method is used by the VDTN protocol to resolve the problems caused by inconsistent connectivity and disturbances in VANETs. The TD3 algorithm is employed for capturing and detecting Worm Hole Attack (WHA) behaviors in VANETs, thereby enhancing security measures. By combining these components, it is possible to create trustworthy and effective communication channels as well as successfully detect and stop rushing attacks inside the VANET. Extensive evaluations and simulations demonstrate the effectiveness of the proposed approach, enhancing both security and communication efficiency.

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Section: Functions Of Td3 In Detecting Wormhole Attacks In Vanetmentioning

confidence: 99%

Enhancing VANET Security: Efficient Communication and Wormhole Attack Detection using VDTN Protocol and TD3 Algorithm

2024

KSII TIIS

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show abstract

“…13,14 It disrupts the fence between data constructions and coatings. 15,16 The regulator is positioned in the control layer. In the controller of SDN, the DDoS attacks concurrently attack its process.…”

Section: Intoductionmentioning

confidence: 99%

“…The SDN's control logic did not generate guidelines for packet promotion 13,14 . It disrupts the fence between data constructions and coatings 15,16 …”

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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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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“…Some specific nodes (anchors) identify the location of their global location, and the remaining nodes control or control their position by measuring the geographic information of their neighbours. This location is used in wireless multihop networks, known as corporate ad hoc localization networks or automatically located network nodes, and its location is determined by sharing its information [5,6].…”

Section: Introductionmentioning

confidence: 99%

Received Signal Strength‐Based Localization for Vehicle Distance Estimation in Vehicular Ad Hoc Networks (VANETs)

et al. 2023

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Vehicular ad hoc networks (VANETs) are an eminent area of intelligent transportation systems (ITS) which includes vehicle tracking, positioning, and emergency warnings. For protective applications, vehicle localization in the urban area is a significant problem. Global Positioning Systems (GPS) are one of the many solutions that have been offered, although they do not offer accuracy. To locate a target vehicle accurately, a unique approach called received signal strength- (RSS-) based localization scheme has been proposed. By detecting signals within its range, it establishes communication through roadside units (RSUs) and determines the typical RSS. Following the discovery of the RSS, the RSS-based localization algorithm helped in determining the precise position of the vehicle. The high signal-to-noise ratio of the proposed algorithm, which is derived from neighbouring RSUs, is its key component. The vehicle’s Cramer Rao lower bound (CRLB) is examined after its position is discovered. Various experimentations considering dynamic vehicles ranging from 2 to 100, localization error, ranging error, and throughput have been conducted which demonstrates that the proposed algorithm (RSS) has better results (87%, 80%, and 120) than other well-established least squares (LS) and weighted least squares methods (WLS).

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RSU-Based Online Intrusion Detection and Mitigation for VANET

Cited by 21 publications

References 70 publications

Enhancing VANET Security: Efficient Communication and Wormhole Attack Detection using VDTN Protocol and TD3 Algorithm

Enhancing VANET Security: Efficient Communication and Wormhole Attack Detection using VDTN Protocol and TD3 Algorithm

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

Received Signal Strength‐Based Localization for Vehicle Distance Estimation in Vehicular Ad Hoc Networks (VANETs)

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