Detection of Position Falsification Attacks in VANETs Applying Trust Model and Machine Learning

Montenegro, Jordan; Iza, Cristhian; Igartua, Mónica Aguilar

doi:10.1145/3416011.3424757

Cited by 22 publications

(7 citation statements)

References 21 publications

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“…The effectiveness of this method is measured based on XGBoost and Random Forest, and the results showed increased efficiency with 10% inaccuracy. In [ 157 ], the authors proposed an ML technique to detect fake position attacks in VANETs. The applied technique is KNN, which is a classification algorithm under supervised learning.…”

Section: Solutions For Security and Trust In Iovmentioning

confidence: 99%

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

Alalwany,

Mahgoub

2024

Sensors

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The Internet of Vehicles (IoV) is a technology that is connected to the public internet and is a subnetwork of the Internet of Things (IoT) in which vehicles with sensors are connected to a mobile and wireless network. Numerous vehicles, users, things, and networks allow nodes to communicate information with their surroundings via various communication channels. IoV aims to enhance the comfort of driving, improve energy management, secure data transmission, and prevent road accidents. Despite IoV’s advantages, it comes with its own set of challenges, particularly in the highly important aspects of security and trust. Trust management is one of the potential security mechanisms aimed at increasing reliability in IoV environments. Protecting IoV environments from diverse attacks poses significant challenges, prompting researchers to explore various technologies for security solutions and trust evaluation methods. Traditional approaches have been employed, but innovative solutions are imperative. Amid these challenges, machine learning (ML) has emerged as a potent solution, leveraging its remarkable advancements to effectively address IoV’s security and trust concerns. ML can potentially be utilized as a powerful technology to address security and trust issues in IoV environments. In this survey, we delve into an overview of IoV and trust management, discussing security requirements, challenges, and attacks. Additionally, we introduce a classification scheme for ML techniques and survey ML-based security and trust management schemes. This research provides an overview for understanding IoV and the potential of ML in improving its security framework. Additionally, it provides insights into the future of trust and security enhancement.

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Section: Solutions For Security and Trust In Iovmentioning

confidence: 99%

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

Alalwany,

Mahgoub

2024

Sensors

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“…The results showed that Random Forest and J48 outperformed other classifiers, including Nave Base, IBK, and Adaboost1. Montenegro et al [17] designed a trust-based model for position falsification attacks based on the k-NN classifier model. In this model, position and received power coherency were used in detecting misbehaving VANETs nodes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The dataset was cleaned and parsed to train and test various ML classifiers. The results obtained were compared to [14,15,17] in terms of precision and recall values, as shown in Table 6. The proposed method showed better performance compared to [14] for all attack types.…”

Section: ) Naïve Bayes Classifiermentioning

confidence: 99%

Ensemble Learning Based Malicious Node Detection in SDN-Based VANETs

Vermani,

Noliya,

Kumar

et al. 2023

J. Inf. Syst. Eng. Bus. Intell.

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Background: The architecture of Software Defined Networking (SDN) integrated with Vehicular Ad-hoc Networks (VANETs) is considered a practical method for handling large-scale, dynamic, heterogeneous vehicular networks, since it offers flexibility, programmability, scalability, and a global understanding. However, the integration with VANETs introduces additional security vulnerabilities due to the deployment of a logically centralized control mechanism. These security attacks are classified as internal and external based on the nature of the attacker. The method adopted in this work facilitated the detection of internal position falsification attacks. Objective: This study aimed to investigate the performance of k-NN, SVM, Naïve Bayes, Logistic Regression, and Random Forest machine learning (ML) algorithms in detecting position falsification attacks using the Vehicular Reference Misbehavior (VeReMi) dataset. It also aimed to conduct a comparative analysis of two ensemble classification models, namely voting and stacking for final decision-making. These ensemble classification methods used the ML algorithms cooperatively to achieve improved classification. Methods: The simulations and evaluations were conducted using the Python programming language. VeReMi dataset was selected since it was an application-specific dataset for VANETs environment. Performance evaluation metrics, such as accuracy, precision, recall, F-measure, and prediction time were also used in the comparative studies. Results: This experimental study showed that Random Forest ML algorithm provided the best performance in detecting attacks among the ML algorithms. Voting and stacking were both used to enhance classification accuracy and reduce time required to identify an attack through predictions generated by k-NN, SVM, Naïve Bayes, Logistic Regression, and Random Forest classifiers. Conclusion: In terms of attack detection accuracy, both methods (voting and stacking) achieved the same level of accuracy as Random Forest. However, the detection of attack using stacking could be achieved in roughly less than half the time required by voting ensemble. Keywords: Machine learning methods, Majority voting ensemble, SDN-based VANETs, Security attacks, Stacking ensemble classifiers, VANETs,

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“…They used supervised learning algorithms including SVM and logistic regression to detect various position falsification attacks. The work in [35] applied machine learning techniques to test the received power coherency metric, which was used as a misbehaving detection metric along with the vehicle position. The authors combined trust value computation with KNN classifier to detect false position coordinates in vehicle messages.…”

Section: A Ml-based Misbehavior Detectionmentioning

confidence: 99%

Machine Learning Based Misbehaviour Detection in VANET Using Consecutive BSM Approach

Sharma

Jaekel

2022

IEEE Open J. Veh. Technol.

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Vehicular ad-hoc network (VANET) is an emerging technology for vehicle-to-vehicle communication vital for reducing road accidents and traffic congestion in an Intelligent Transportation System (ITS). VANET communication is vulnerable to various attacks and cryptographic techniques are commonly used for message integrity and authentication of vehicles. However, cryptograhpic techniques alone may not be sufficient to protect against insider attacks. Many VANET safety applications rely on periodic broadcast of basic safety messages (BSMs) from surrounding vehicles that contain important status information about a vehicle such as its position, speed, and heading. If an attacker (misbehaving vehicle) injects false position information in a BSM, it can lead to serious consequences including traffic congestion or even accidents. Therefore, it is imperative to accurately detect and identify such attackers to ensure safety in the network. This paper presents a novel data-centric approach to detect position falsification attacks, using machine learning (ML) algorithms. Unlike existing techniques, the proposed approach combines information from 2 consecutive BSMs for training and testing. Simulations using the Vehicular Reference Misbehavior (VeReMi) dataset demonstrate that the proposed model clearly outperforms existing approaches for identifying a range of different attack types.

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Detection of Position Falsification Attacks in VANETs Applying Trust Model and Machine Learning

Cited by 22 publications

References 21 publications

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

Ensemble Learning Based Malicious Node Detection in SDN-Based VANETs

Machine Learning Based Misbehaviour Detection in VANET Using Consecutive BSM Approach

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