DDoS Attack Detection in Vehicular Ad-Hoc Network (VANET) for 5G Networks

Gaurav, Akshat; Gupta, Brij B.; Peñalvo, Francisco José García; Nedjah, Nadia; Psannis, Kostas E.

doi:10.1007/978-3-030-85428-7_11

Cited by 17 publications

(13 citation statements)

References 67 publications

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“…3 Using various techniques, attackers corrupt, update, or delete data or information. 4 SDN is a framework designed to build a flexible network and is easier to manage. 5 It consolidates the control by decoupling the regulator function from the advancing function in the different systems of a network.…”

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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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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“…Several trust-based defense mechanisms for DDoS attacks in VANET [ 43 , 44 , 45 ] rely on the trust score of neighboring vehicles. These studies propose fixed and learning-based trust mechanisms for different VANET scenarios.…”

Section: Related Workmentioning

confidence: 99%

RSU-Based Online Intrusion Detection and Mitigation for VANET

Haydari

Yılmaz

2022

Sensors

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Secure vehicular communication is a critical factor for secure traffic management. Effective security in intelligent transportation systems (ITS) requires effective and timely intrusion detection systems (IDS). In this paper, we consider false data injection attacks and distributed denial-of-service (DDoS) attacks, especially the stealthy DDoS attacks, targeting integrity and availability, respectively, in vehicular ad-hoc networks (VANET). Novel machine learning techniques for intrusion detection and mitigation based on centralized communications through roadside units (RSU) are proposed for the considered attacks. The performance of the proposed methods is evaluated using a traffic simulator and a real traffic dataset. Comparisons with the state-of-the-art solutions clearly demonstrate the superior detection and localization performance of the proposed methods by 78% in the best case and 27% in the worst case, while achieving the same level of false alarm probability.

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“…In Gaurav et al. ( 2022 ), the authors propose a fog-based DDoS detection approach that uses fuzzy logic to differentiate attack traffic from normal traffic in 5G-enabled smart cities. They describe the DDoS attack at VANET (Vehicular Ad Hoc Network (VANET)) systems that is one of the cyber-attacks that attack the availability of such systems, since the vehicle nodes are not capable of exchanging valuable information.…”

Section: Literature Review and Contributionsmentioning

confidence: 99%

A constrained optimization model for the provision of services in a 5G network with multi-level cybersecurity investments

et al. 2022

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In this paper, we present a multi-tiered network-based optimization model describing the provision of services by network slices of 5G-Service providers (e.g. through Unmanned Aerial Vehicles (UAVs) organized as Flying Ad hoc Networks (FANET)), taking into account the security levels of each provider. The three levels of the network consist of the infrastructure layers, which contain resources needed to execute a service, the slices layer, where services are served for the services layer, which represents the upper layer of the network and consists of services or applications required by users or devices. The objective of the proposed model is to establish the optimal flows between network layers and the optimal security levels in order to maximize the providers’ profits, given by the difference between the revenues obtained by the sale of services and the rental of their resources and the costs. Numerical experiments are performed and solved with a new nature-inspired genetic algorithm adapted to the optimization 5G network problem.

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DDoS Attack Detection in Vehicular Ad-Hoc Network (VANET) for 5G Networks

Cited by 17 publications

References 67 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

RSU-Based Online Intrusion Detection and Mitigation for VANET

A constrained optimization model for the provision of services in a 5G network with multi-level cybersecurity investments

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