Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues

Danba, Sedeng; Bao, Jianchun; Han, Guorong; Guleng, Siri; Wu, Celimuge

doi:10.3390/s22186995

Cited by 8 publications

(7 citation statements)

References 172 publications

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“…Information on the Sybil attack and the necessary protocol layer is provided in. 6 Improving wireless boundary and software in V2V communication mode can help mitigate or prevent this attack. 7 Sybil attacks in VANETs (Vehicular Ad-hoc Networks) differ significantly from those in conventional Ad-Hoc networks due to VANETs' high mobility, which allows for more effective creation of fake identities.…”

Section: Introductionmentioning

confidence: 99%

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Developing an optimized routing protocol with rumor riding technique for detection of Sybil attack in VANET environment

Navinkumar,

Somasundaram

2024

Int J Communication

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SummaryAn attack on VANET known as a Sybil attack involves the attacker claiming or stealing multiple identities with the intent of causing havoc on the network's operation by disseminating false information. To protect the VANET network from Sybil attack, a variety of methods have been proposed. In order to compensate for the Sybil attack on VANETs, a rumor riding with salp swarm optimization (SSO) based Routing protocol is developed in this research work. The primary goal of this investigation is to discover how VANETs are vulnerable to the Sybil attack in order to improve their security. Rumor riding will be used to identify the Sybil attack in the suggested strategy. VANETs are protected by the Blowfish Algorithm, which uses SSO to select the best key for each network. The collective intelligence and dynamic adaptation of SALP swarm optimization algorithm enable efficient identification of rumor sources in complex networks. The algorithm is scalable, flexible, and adept at reducing false positives and negatives, making it suitable for large‐scale monitoring. Its parallel processing capabilities and robustness ensure rapid and reliable detection. Using the NS2 platform, the proposed method is compared to existing benchmark in terms of delay, packet delivery ratio (PDR), packet loss, throughput, and attack detection rate. The proposed SSO has reduced the packet loss by nearly 5% and delay by nearly 7%, when compared with existing techniques. The delivery ratio of packet for proposed model is increased up to 8% of PDR, when compared with ACO, PSO, and FA. According to this evidence, it is clear that the proposed SSO outperformed existing approaches in a variety of performance metrics.

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Section: Introductionmentioning

confidence: 99%

“…Cars can either generate new identities for other vehicles or take their individualities and use them to mislead other vehicles. Information on the Sybil attack and the necessary protocol layer is provided in 6 . Improving wireless boundary and software in V2V communication mode can help mitigate or prevent this attack 7 .…”

Section: Introductionmentioning

confidence: 99%

Developing an optimized routing protocol with rumor riding technique for detection of Sybil attack in VANET environment

Navinkumar,

Somasundaram

2024

Int J Communication

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“…However, in the case of UAVs, autonomy is established more widely than in ground vehicles, such as cars. Building on advancements in communication technologies [6], hardware [7] and software, particularly artificial intelligence [8], the usage of (connected) vehicles is predicted to grow in consumer, civil, and military applications [9].…”

Section: Introductionmentioning

confidence: 99%

The Evolution of Intelligent Transportation Systems: Analyzing the Differences and Similarities between IoV and IoFV

Herich,

Vaščák

2024

Drones

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The Internet of Vehicles (IoV) and the Internet of Flying Vehicles (IoFV) are integral components of intelligent transportation systems with the potential to revolutionize the way we move people and goods. Although both the IoV and IoFV share a common goal of improving transportation efficiency, safety, and sustainability, they possess distinct characteristics and face unique challenges. To date, the existing literature has predominantly focused on specific aspects of either the IoV or IoFV, but a comprehensive review comparing and contrasting the two domains is still lacking. This review paper aims to address this gap by providing an in-depth analysis of the key differences between the IoV and IoFV systems. The review will examine the technological components, network infrastructure, communication protocols, data management, objectives, applications, challenges, and future trends associated with both domains. Additionally, this paper will explore the potential impact of technologies such as artificial intelligence, machine learning, and blockchain. Ultimately, the paper aims to contribute to a deeper understanding of the implications and potential of these technologies, both in the context of transportation systems and beyond.

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“…• Big data management: Effective management of big data poses a significant challenge within the context of FL-enabled IDSs in IoV. The sheer volume, velocity, and diversity of data generated by vehicles require robust storage, processing, and analysis capabilities [69]. The integration of FL-enabled IDSs necessitates the use of extensive data for training and model updates.…”

mentioning

confidence: 99%

“…This variability can potentially disrupt the FL environment, affecting the consistency and accuracy of the IDS models. Maintaining stability requires the implementation of robust systems to address fluctuations in participation rates, network disconnections, and intermittent data availability [69]. Future research aims to develop algorithms that can adapt dynamically to changes in the network, ensuring the stability of FL models, even when confronted with evolving IoV scenarios.…”

mentioning

confidence: 99%

Federated Learning for Intrusion Detection Systems in Internet of Vehicles: A General Taxonomy, Applications, and Future Directions

Alsamiri,

Alsubhi

2023

Future Internet

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In recent years, the Internet of Vehicles (IoV) has garnered significant attention from researchers and automotive industry professionals due to its expanding range of applications and services aimed at enhancing road safety and driver/passenger comfort. However, the massive amount of data spread across this network makes securing it challenging. The IoV network generates, collects, and processes vast amounts of valuable and sensitive data that intruders can manipulate. An intrusion detection system (IDS) is the most typical method to protect such networks. An IDS monitors activity on the road to detect any sign of a security threat and generates an alert if a security anomaly is detected. Applying machine learning methods to large datasets helps detect anomalies, which can be utilized to discover potential intrusions. However, traditional centralized learning algorithms require gathering data from end devices and centralizing it for training on a single device. Vehicle makers and owners may not readily share the sensitive data necessary for training the models. Granting a single device access to enormous volumes of personal information raises significant privacy concerns, as any system-related problems could result in massive data leaks. To alleviate these problems, more secure options, such as Federated Learning (FL), must be explored. A decentralized machine learning technique, FL allows model training on client devices while maintaining user data privacy. Although FL for IDS has made significant progress, to our knowledge, there has been no comprehensive survey specifically dedicated to exploring the applications of FL for IDS in the IoV environment, similar to successful systems research in deep learning. To address this gap, we undertake a well-organized literature review on IDSs based on FL in an IoV environment. We introduce a general taxonomy to describe the FL systems to ensure a coherent structure and guide future research. Additionally, we identify the relevant state of the art in FL-based intrusion detection within the IoV domain, covering the years from FL’s inception in 2016 through 2023. Finally, we identify challenges and future research directions based on the existing literature.

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Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues

Cited by 8 publications

References 172 publications

Developing an optimized routing protocol with rumor riding technique for detection of Sybil attack in VANET environment

Developing an optimized routing protocol with rumor riding technique for detection of Sybil attack in VANET environment

The Evolution of Intelligent Transportation Systems: Analyzing the Differences and Similarities between IoV and IoFV

Federated Learning for Intrusion Detection Systems in Internet of Vehicles: A General Taxonomy, Applications, and Future Directions

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