A Machine Learning Framework for Intrusion Detection in VANET Communications

Rabah, Nourhene Ben; Idoudi, Hanen

doi:10.1007/978-3-031-09640-2_10

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…In these cases, malicious edges are found utilizing density of sub-graphs, structural, temporal, and content information extraction, and a greedy search strategy, respectively. A probabilistic method of detection was utilized to propose an anomaly node detection method in [22].…”

Section: Literature Reviewmentioning

confidence: 99%

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Intelligent analytics algorithms in breach detection systems for securing VANETs and data for smart transportation management

Bhuvana

Hashmi

Adhvaryu

et al. 2023

Preprint

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In-vehicle communication has developed into a crucial element of today's driving environment as a result of the expanding additions of sensor-centric communication as well as computing devices inside a vehicle for a variety of purposes, consists of vehicle monitoring, physical wiring minimization as well as driving efficiency. The relevant literature on cyber security for in-vehicle communication methods does not, however, currently offer any certain solutions for in-vehicle cyber hazards. The existing solutions, which mostly rely on protocol-specific security approaches, do not provide a comprehensive security framework for in-vehicle communication. This study aims to develop an effective data transmission and intelligent machine learning technique for smart vehicle management in VANET breach detection. In this study, ensemble adversarial Boltzmann CNN architecture is used to detect breaches. The secure short hop opportunistic local routing protocol is then used to send the data. Throughput, QoS, training accuracy, validation accuracy, and network security analysis are all part of the experimental analysis for a variety of security-based datasets. the proposed technique attainedthroughput of 88%, QoS of 77%, training accuracy of 93%, validation accuracy of 96%, network security analysis of 63%, scalability of 75%.

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Section: Literature Reviewmentioning

confidence: 99%

“…As a result, the following are the visible and concealed unit state marginal probability distributions by eq. (21,22):…”

Section: Breach Detection Using Ensemble Adversarial Boltzmann Convol...mentioning

confidence: 99%

Intelligent analytics algorithms in breach detection systems for securing VANETs and data for smart transportation management

Bhuvana

Hashmi

Adhvaryu

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The GBO method uses a key component to achieve a balanced exploration of important search space regions while getting close to global and near-optimal spots. The use of ρ is described in terms of Equations ( 7)- (9).…”

Section: Gradient Search Rule (Gsr)mentioning

confidence: 99%

“…The IDS sounds an alarm when these patterns are present in network traffic. Misuse-based IDS are effective at spotting well-known attacks, but they may struggle to deal with brand-new or unidentified attack patterns [9]. Furthermore, it can be resource-intensive to maintain and update the signature database.…”

Section: Introductionmentioning

confidence: 99%

Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic

Manderna,

Kumar,

Dohare

et al. 2023

Sensors

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Vehicle malfunctions have a direct impact on both human and road safety, making vehicle network security an important and critical challenge. Vehicular ad hoc networks (VANETs) have grown to be indispensable in recent years for enabling intelligent transport systems, guaranteeing traffic safety, and averting collisions. However, because of numerous types of assaults, such as Distributed Denial of Service (DDoS) and Denial of Service (DoS), VANETs have significant difficulties. A powerful Network Intrusion Detection System (NIDS) powered by Artificial Intelligence (AI) is required to overcome these security issues. This research presents an innovative method for creating an AI-based NIDS that uses Deep Learning methods. The suggested model specifically incorporates the Self Attention-Based Bidirectional Long Short-Term Memory (SA-BiLSTM) for classification and the Cascaded Convolution Neural Network (CCNN) for learning high-level features. The Multi-variant Gradient-Based Optimization algorithm (MV-GBO) is applied to improve CCNN and SA-BiLSTM further to enhance the model’s performance. Additionally, information gained using MV-GBO-based feature extraction is employed to enhance feature learning. The effectiveness of the proposed model is evaluated on reliable datasets such as KDD-CUP99, ToN-IoT, and VeReMi, which are utilized on the MATLAB platform. The proposed model achieved 99% accuracy on all the datasets.

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Classification of Distributed Denial of Service Attacks in VANET: A Survey

Krishna

Karri

2023

Wireless Pers Commun

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A Machine Learning Framework for Intrusion Detection in VANET Communications

Cited by 4 publications

References 17 publications

Intelligent analytics algorithms in breach detection systems for securing VANETs and data for smart transportation management

Intelligent analytics algorithms in breach detection systems for securing VANETs and data for smart transportation management

Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic

Classification of Distributed Denial of Service Attacks in VANET: A Survey

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