AntibotV: A Multilevel Behaviour-Based Framework for Botnets Detection in Vehicular Networks

Rahal, Rabah; Korba, Abdelaziz Amara; Ghoualmi-Zine, Nacira; Challal, Yacine; Ghamri-Doudane, Mohamed Yacine

doi:10.1007/s10922-021-09630-8

Cited by 10 publications

(3 citation statements)

References 56 publications

(106 reference statements)

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“…Rahal et al [93] introduced a process for botnet detection in VANETs that monitors vehicle and in-vehicle activity to detect DDoS attacks and Eavesdropping. The proposed work uses ML based algorithms (SVM, k-NN) and performs 99.4% accuracy at finding botnets.…”

Section: B Inter-vehicle Levelmentioning

confidence: 99%

Machine Learning Use-Cases in C-ITS Applications

Bereczki¹,

Simon²

2023

Infocommunications journal

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In recent years, the development of Cooperative Intelligent Transportation Systems (C-ITS) have witnessed significant growth thus improving the smart transportation concept. The ground of the new C-ITS applications are machine learning algorithms. The goal of this paper is to give a structured and comprehensive overview of machine learning use-cases in the field of C-ITS. It reviews recent novel studies and solutions on CITS applications that are based on machine learning algorithms. These works are organised based on their operational area, including self-inspection level, inter-vehicle level and infrastructure level. The primary objective of this paper is to demonstrate the potential of artificial intelligence in enhancing C-ITS applications.

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Section: B Inter-vehicle Levelmentioning

confidence: 99%

Machine Learning Use-Cases in C-ITS Applications

Bereczki¹,

Simon²

2023

Infocommunications journal

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“…But, the performance evaluation was based on accuracy only. [75] proposed an AntibotV framework based on multi-layer behaviors, which constructs a detection system by collecting network traffic data from legitimate and malicious applications. The decision tree algorithm is used to train vehicle-mounted data and effectively monitor vehicle activities in the network.…”

Section: ) the Solutions To Malicious Attacksmentioning

confidence: 99%

A Survey on Vehicular Cloud Network Security

Deng,

Liu

et al. 2023

IEEE Access

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The novel Vehicular Cloud Network (VCN) is a close combination of Vehicular Ad hoc Network, Cloud Computing, and Edge Computing. However, VCN is facing the enormous challenge of security and privacy before it is generalized. To address these issues, VCN communication characteristics and security requirements are summarized first. Secondly, the paper conducts an in-depth analysis of the existing security problems and solutions in VCN based on several attack models, including routing, data, identity, DoS, and hostile attacks. And we summarize the security solutions into two categories: security defense measures based on identity authentication and access control, and security protection measures based on traffic detection and anomaly detection. Thirdly, this paper proposes a reliable VCN security architecture consisting of three types of clouds: vehicular, edge, and central clouds. Moreover, the VCN architecture combines a Vehicle Intrusion Detection and Prevention System with a Vehicular Cloud Security Management Platform to ensure the security of VCN. Last but not least, some open issues worth investigating are discussed.

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“…Various Machine Learning (ML)/ Deep Learning (DL) based Intrusion Detection Systems (ML/DL-based IDSs) have been proposed to protect vehicular networks against attacks. Most of them rely on supervised and centralized learning [6].…”

Section: Introductionmentioning

confidence: 99%

Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks

Amara Korba,

Boualouache,

Brik

et al. 2023

ICC 2023 - IEEE International Conference on Communications

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Le déploiement de véhicules connectés et automatisés (CAV) utilisant les réseaux 5G et 5G-B les rend vulnérables à de nouveaux vecteurs d'attaques. Afin de détecter et de mitiger ces attaques, plusieurs systèmes de détection d'intrusions (IDS) basés sur l'apprentissage automatique ont été proposés, dont la grande majorité utilise l'apprentissage profond supervisé. Cependant, la principale limite de ce type de solution est son incapacité à détecter des attaques différentes de celles vues lors de l'apprentissage de l'IDS, ou de nouvelles attaques, également appelées attaques Zero-day. De plus, l'apprentissage du modèle de détection nécessite une collecte et un étiquetage importants des données, ce qui augmente le coût de communication et soulève des problèmes de confidentialité. Afin de pallier ces problèmes, nous proposons dans cet article un nouveau IDS basé sur l'apprentissage fédéré non supervisé préservant la confidentialité, et dont l'apprentissage du système ne nécessite que le trafic réseau légitime. L'expérimentation de la solution proposée sur un jeu de données récent montre que le système proposé présente un taux de détection élevé tout en minimisant le taux de faux positifs et le délai de détection.

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AntibotV: A Multilevel Behaviour-Based Framework for Botnets Detection in Vehicular Networks

Cited by 10 publications

References 56 publications

Machine Learning Use-Cases in C-ITS Applications

Machine Learning Use-Cases in C-ITS Applications

A Survey on Vehicular Cloud Network Security

Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks

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