Unsupervised Learning Algorithms for Denial of Service Detection in Vehicular Networks

Attar, Ali El; Fadlallah, Ahmad; Chbib, Fadlallah; Khatoun, Rida

doi:10.1109/iceccme55909.2022.9987992

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…For a car-hacking dataset, the suggested scheme’s performance evaluation findings demonstrate a high performance [ 143 ]. The authors in [ 144 ] addressed the significant issue of DoS attacks in vehicular networks and investigated the viability of using unsupervised learning algorithms for DoS detection. Since DoS attacks pose threats to connected car functionality and safety, their detection and mitigation are of utmost importance.…”

Section: Solutions For Security and Trust In Iovmentioning

confidence: 99%

“…Testing in the actual world would be beneficial to extend the experiments to IoV environments in the real world. Testing in the field can provide information about the applicability of machine-learning-based security solutions in the real world and their effectiveness in real-world vehicular networks [ 139 , 141 , 142 , 143 , 144 ]. Researchers can refine and optimize algorithms to ensure reliability in real-world scenarios.…”

Section: Solutions For Security and Trust In Iovmentioning

confidence: 99%

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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%