TFMD-SDVN: a trust framework for misbehavior detection in the edge of software-defined vehicular network

Nayak, Rajendra Prasad; Sethi, Srinivas; Bhoi, Sourav Kumar; Mohapatra, Debasis; Sahoo, Rashmita; Sharma, Pradip Kumar; Puthal, Deepak

doi:10.1007/s11227-021-04227-z

Cited by 8 publications

(2 citation statements)

References 54 publications

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“…Over the past years, several security methods have been investigated to identify and address these misbehaviors in VANETs. The proposed Trust-Based Event Detection Algorithm (TB-EDA) compares the trust values of the neighboring cars of a node with the threshold trust value measured to identify misbehaviors [10]. In study [11], the authors introduced the Vehicular Reference Misbehavior dataset (VeReMi) to assess various misbehavior detectors.…”

Section: Existing Workmentioning

confidence: 99%

A Neuro-Genetic Security Framework for Misbehavior Detection in VANETs

Naqvi,

Chaudhary,

Kumar

2024

IJACSA

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Genetic Algorithm (GA) is an excellent optimization algorithm which has attracted the attention of researchers in various fields. Many papers have been published on works done on GA, but no single paper ever utilized this algorithm for misbehavior detection in VANETs. This is because GA requires manual definition of fitness function and defining a fitness function for VANETs is a complex task. Automating the creation of these fitness functions is still a difficulty, even though studies have found several successful applications of GA. In this study, a neuro-genetic security framework has been built with ANN classifier for detecting misbehavior in VANETs. It leverages a genetic algorithm for feature reduction with ANN as a dynamic fitness function, considering both node behaviors and contextual GPS data. Deployed at the Roadside Unit (RSU) level, the framework detects misbehaving nodes, broadcasting alerts to RSUs, Central Authority and the vehicles. The ANN based fitness function has been employed in GA that enabled the GA to select the best results. The 10-fold CV used enabled the whole system to be unbiased giving a precision accuracy of 0.9976 with recall and F1 scores as 0.9977, and 0.9977 respectively. Comparative evaluations, using the VeReMi Extension dataset, demonstrate the framework's superiority in precision, recall, and F1 score for binary and multiclass classification. This hybrid genetic algorithm with ANN fitness function presents a robust, adaptive solution for VANET misbehavior detection. Its context-aware nature accommodates dynamic scenarios, offering an effective security framework for the evolving threats in vehicular environments.

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Section: Existing Workmentioning

confidence: 99%

A Neuro-Genetic Security Framework for Misbehavior Detection in VANETs

Naqvi,

Chaudhary,

Kumar

2024

IJACSA

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“…The node deprivation attack is similar to the replay attack in that it starts with the capture of the legitimate mesh router's deauthentication request message. After that, an attacker replays the deauthentication request message in order to isolate the mesh router when it rejoins the network [33][34][35].…”

Section: Security Analysismentioning

confidence: 99%

Efficient Key Management Mechanism with Trusted Gateways for Wireless Mesh Networks

Karri¹,

Prabu

Routray

et al. 2022

Wireless Communications and Mobile Computing

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Securing wireless mesh networks (WMNs) is a crucial issue due to its intrinsic characteristics. Several endangered features might emerge due to the exposure of the networks to a diversity of medium access control (MAC) layers such as distributed denial of service (DDoS) attacks, false reply attacks, and other identity attacks. Against these attacks, the determination of existing techniques is insufficient to ensure the complete security solutions to protect the backbone network at multiple levels. As a result, effective, scalable, and integrated security solutions for WMNs are required. In WMNs, protecting legitimate gateway nodes and internal mesh routers against malicious attacks at the MAC layer remains a difficult problem. Our proposed trust-based security mechanism includes distributed authentication and deauthentication algorithms that validates backbone mesh routers as well as gateway nodes. Particularly, this proposed model targets DDoS attacks in the network. The proposed DDoS attack prevention mechanism (DAPM) uses distributed authentication and deauthentication algorithms to build trusted group heads for managing secure data communication in the network. Our research and practical results show that the proposed mechanism decreases the severity of malicious nodes and strengthens the security compared to existing centralized schemes such as digital signature authentication (DSA-Mesh, MENSA, Mobisec, and AHKM). The experimental solutions show the significance of the proposed work with 10% to 12% of better performance than the existing techniques.

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VANET authentication protocols: security analysis and a proposal

Piramuthu

Caesar²

2022

J Supercomput

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TFMD-SDVN: a trust framework for misbehavior detection in the edge of software-defined vehicular network

Cited by 8 publications

References 54 publications

A Neuro-Genetic Security Framework for Misbehavior Detection in VANETs

A Neuro-Genetic Security Framework for Misbehavior Detection in VANETs

Efficient Key Management Mechanism with Trusted Gateways for Wireless Mesh Networks

VANET authentication protocols: security analysis and a proposal

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