SHIELDNET: An Adaptive Detection Mechanism against Vehicular Botnets in VANETs

Garip, Mevlut Turker; Lin, Jenshan; Reiher, Peter; Gerla, Mário

doi:10.1109/vnc48660.2019.9062790

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…Garip et al [12] proposed an adaptive detection mechanism, called SHIELDNET, to identify botnets on the network. For the identification of DDoS attacks (i.e., brute force, TCP-SYN, UDP, and HTTP floods), Nie et al [13] used Convolutional Neural Network (CNN) for the development of a data-driven IDS, i.e., a hybrid IDS that is signature-based and anomalybased, to identify the DDoS attack in the exchange of messages between On-Board Units (OBU) and RSUs.…”

Section: Related Workmentioning

confidence: 99%

An edge-based smart network monitoring system for the Internet of Vehicles

Magaia

Pereira

2022

ICC 2022 - IEEE International Conference on Communications

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The Internet of Vehicles (IoV) is the future of transportation. It will be present everywhere and will have a huge impact on our lives. However, there are plenty of aspects to consider while studying these networks, such as data dissemination, cybersecurity threats and vulnerabilities. For an IoV to work efficiently, data needs to spread through it efficiently. However, the dynamics of vehicular environments due to frequent node mobility and nodes' misbehavior poses many challenges to efficient data dissemination. Therefore, a deep learning-based monitoring system that is capable of detecting anomalies in the network and identifying known misbehavior is proposed. Performance evaluation shows that the monitoring system can identify well-known attacks with a very high success rate. Besides, the algorithm is also capable of detecting other types of misbehavior without labeling them.

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Section: Related Workmentioning

confidence: 99%

An edge-based smart network monitoring system for the Internet of Vehicles

Magaia

Pereira

2022

ICC 2022 - IEEE International Conference on Communications

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“…M. T. Garip et al presented SHIELDNET, in which they use ML algorithms for the detection of a vehicle botnet communication protocol called GHOST, which endanger traffic safety in VANETs. According to the simulation results, they showed that the proposed framework correctly identified 77% of the bots [40].…”

Section: A Trust Managementmentioning

confidence: 99%

A Review on Machine Learning Techniques Used in VANET and FANET Networks

MUTİ

Ülkü

2022

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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The widespread use of the Internet and the increase in the number and variety of devices connected to the internet have led to the emergence of new methods in wireless communication. Dynamic and temporary Ad-Hoc networks, which do not require a fixed infrastructure as in traditional wireless network communication, are one of these new methods. The fact that Ad-Hoc networks do not need a fixed infrastructure has revealed a network structure with a lower cost and less configuration. Mobile Ad-Hoc networks play an important role, especially in the communication of nodes on the move. FANET (Flying Ad-Hoc Networks) networks, which are called flying ad hoc networks, are mobile Ad-Hoc networks used for communication of unmanned aerial vehicles (UAV), and VANET (Vehicular Ad-Hoc Networks) networks, which are called vehicular ad hoc networks, are mobile Ad-Hoc networks used for communication of road vehicles. The development and dissemination of these networks make a significant contribution to the development of autonomous vehicles and UAVs. The increase in the use of FANET and VANET networks, which are specialized subnets of mobile Ad-Hoc networks, and the increase in the number of nodes in these networks have caused problems related to security, efficiency, and sustainability in these networks. Machine learning methods, one of today' s effective and common approaches, are one of the ways that are frequently used in solving the problems specified in FANET and VANET networks. The rapid topology change, which is one of the most important features of these networks, makes it difficult to provide traffic management, trust management, routing, and data transmission. In this direction, machine learning approaches play an active role. In this study, it is presented by examining which machine learning techniques are used in the literature to perform important tasks such as traffic management, trust management, routing, and data transfer. Thus, it is aimed for those who will work in these fields to acquire information about machine learning approaches that can be used. Since the FANET network type is a new approach, it has been observed that there are few studies using machine learning. In VANET systems, studies using machine learning methods are especially intense in 2021. This study was carried out to give the reader an idea about which machine learning methods can be used in which problems in FANET and VANET networks.

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“…Describes specific botnet detection mechanisms, advantages, disadvantages, for instance, the Shieldnet framework to detect botnets in vehicular networks [12]. [13,14] Focuses on describing different kinds of botnet attacks ( [13]) and on the threats represented by botnets ( [14]), without going into specific mitigation strategies.…”

Section: Paper Main Contribution and Reference Metricsmentioning

confidence: 99%

“…The paper also proposes the use of localisation mechanisms to limit the exposure to far-away botnets. Reference [12] introduces Shieldnet, which employs a set of machine learning algorithms to detect the use of the GHOST [81] vehicular botnet. The algorithm detects suspicious activity by searching for outlier data within the Basic Safety Messages (BSM) fields of VANET broadcasts, also isolating known infected hosts using a reputation-based identification system.…”

Section: Vehicle Networkmentioning

confidence: 99%

A Survey on Botnets: Incentives, Evolution, Detection and Current Trends

Stege

El-Habr

et al. 2021

Future Internet

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Botnets, groups of malware-infected hosts controlled by malicious actors, have gained prominence in an era of pervasive computing and the Internet of Things. Botnets have shown a capacity to perform substantial damage through distributed denial-of-service attacks, information theft, spam and malware propagation. In this paper, a systematic literature review on botnets is presented to the reader in order to obtain an understanding of the incentives, evolution, detection, mitigation and current trends within the field of botnet research in pervasive computing. The literature review focuses particularly on the topic of botnet detection and the proposed solutions to mitigate the threat of botnets in system security. Botnet detection and mitigation mechanisms are categorised and briefly described to allow for an easy overview of the many proposed solutions. The paper also summarises the findings to identify current challenges and trends within research to help identify improvements for further botnet mitigation research.

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SHIELDNET: An Adaptive Detection Mechanism against Vehicular Botnets in VANETs

Cited by 11 publications

References 11 publications

An edge-based smart network monitoring system for the Internet of Vehicles

An edge-based smart network monitoring system for the Internet of Vehicles

A Review on Machine Learning Techniques Used in VANET and FANET Networks

A Survey on Botnets: Incentives, Evolution, Detection and Current Trends

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