Softwarization, Virtualization, and Machine Learning for Intelligent and Effective Vehicle-to-Everything Communications

Moubayed, Abdallah; Shami, Abdallah

doi:10.1109/mits.2020.3014124

Cited by 23 publications

(17 citation statements)

References 74 publications

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“…To protect the vehicle from being breached, all packets or messages transmitted in the protected vehicular network are captured by packet taps or sniffers (e.g., NetFlow), and then analyzed by the proposed IDS before being forwarded to the protected vehicle [35]. For example, if an attacker is launching a DoS attack by sending a large volume of malicious traffic to a vehicle system, the malicious traffic will be detected by the proposed IDS by processing the traffic data captured by the sniffer; alarms will then be generated, and the attacker's access will be denied, therefore protecting the vehicle from being breached [36]- [38].…”

Section: Attack Scenarios and Ids Deploymentmentioning

confidence: 99%

MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles

Yang

Moubayed

Shami

2022

IEEE Internet Things J.

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164

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Modern vehicles, including connected vehicles and autonomous vehicles, nowadays involve many electronic control units connected through intra-vehicle networks to implement various functionalities and perform actions. Modern vehicles are also connected to external networks through vehicle-toeverything technologies, enabling their communications with other vehicles, infrastructures, and smart devices. However, the improving functionality and connectivity of modern vehicles also increase their vulnerabilities to cyber-attacks targeting both intra-vehicle and external networks due to the large attack surfaces. To secure vehicular networks, many researchers have focused on developing intrusion detection systems (IDSs) that capitalize on machine learning methods to detect malicious cyberattacks. In this paper, the vulnerabilities of intra-vehicle and external networks are discussed, and a multi-tiered hybrid IDS that incorporates a signature-based IDS and an anomaly-based IDS is proposed to detect both known and unknown attacks on vehicular networks. Experimental results illustrate that the proposed system can detect various types of known attacks with 99.99% accuracy on the CAN-intrusion-dataset representing the intra-vehicle network data and 99.88% accuracy on the CICIDS2017 dataset illustrating the external vehicular network data. For the zero-day attack detection, the proposed system achieves high F1-scores of 0.963 and 0.800 on the above two datasets, respectively. The average processing time of each data packet on a vehicle-level machine is less than 0.6 ms, which shows the feasibility of implementing the proposed system in real-time vehicle systems. This emphasizes the effectiveness and efficiency of the proposed IDS.

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Section: Attack Scenarios and Ids Deploymentmentioning

confidence: 99%

MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles

Yang

Moubayed

Shami

2022

IEEE Internet Things J.

Self Cite

164

View full text Add to dashboard Cite

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“…VLC technology proposes many benefits including high data transmission (up to 10 Gbps), a low level interference range, and considerable vigor against commonly known jamming attacks in wireless communications. However, pitfalls still exist within the technology, as VLC is susceptible to noises formed by additional light sources, which makes it inconvenient in adverse weather conditions and unable to handle non-LOS situations [155,156].…”

Section: Opportunitiesmentioning

confidence: 99%

Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities

Abdelkader

Elgazzar

Khamis

2021

Sensors

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In an effort to reach accident-free milestones or drastically reduce/eliminate road fatalities rates and traffic congestion and to create disruptive, transformational mobility systems and services, different parties (e.g., automakers, universities, governments, and road traffic regulators) have collaborated to research, develop, and test connected vehicle (CV) technologies. CVs create new data-rich environments and are considered key enablers for many applications and services that will make our roads safer, less congested, and more eco-friendly. A deeper understanding of the CV technologies will pave the way to avoid setbacks and will help in developing more innovative applications and breakthroughs. In the CV paradigm, vehicles become smarter by communicating with nearby vehicles, connected infrastructure, and the surroundings. This connectivity will be substantial to support different features and systems, such as adaptive routing, real-time navigation, and slow and near real-time infrastructure. Further examples include environmental sensing, advanced driver-assistance systems, automated driving systems, mobility on demand, and mobility as a service. This article provides a comprehensive review on CV technologies including fundamental challenges, state-of-the-art enabling technologies, innovative applications, and potential opportunities that can benefit automakers, customers, and businesses. The current standardization efforts of the forefront enabling technologies, such as Wi-Fi 6 and 5G-cellular technologies are also reviewed. Different challenges in terms of cooperative computation, privacy/security, and over-the-air updates are discussed. Safety and non-safety applications are described and possible future opportunities that CV technology brings to our life are also highlighted.

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“…Softwarization, virtualization, and machine learning concepts are also introduced to support vehicular communication. These concepts can be employed to enable mobile big data in V2X [ 26 ].…”

Section: Vehicular Network Preliminarymentioning

confidence: 99%

Role of Machine Learning in Resource Allocation Strategy over Vehicular Networks: A Survey

Nurcahyani

Lee

2021

Sensors

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The increasing demand for smart vehicles with many sensing capabilities will escalate data traffic in vehicular networks. Meanwhile, available network resources are limited. The emergence of AI implementation in vehicular network resource allocation opens the opportunity to improve resource utilization to provide more reliable services. Accordingly, many resource allocation schemes with various machine learning algorithms have been proposed to dynamically manage and allocate network resources. This survey paper presents how machine learning is leveraged in the vehicular network resource allocation strategy. We focus our study on determining its role in the mechanism. First, we provide an analysis of how authors designed their scenarios to orchestrate the resource allocation strategy. Secondly, we classify the mechanisms based on the parameters they chose when designing the algorithms. Finally, we analyze the challenges in designing a resource allocation strategy in vehicular networks using machine learning. Therefore, a thorough understanding of how machine learning algorithms are utilized to offer a dynamic resource allocation in vehicular networks is provided in this study.

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Softwarization, Virtualization, and Machine Learning for Intelligent and Effective Vehicle-to-Everything Communications

Cited by 23 publications

References 74 publications

MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles

MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles

Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities

Role of Machine Learning in Resource Allocation Strategy over Vehicular Networks: A Survey

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