RTED-SD: A Real-Time Edge Detection Scheme for Sybil DDoS in the Internet of Vehicles

Li, Jiabin; Zhi, Xue; Li, Changlian; Liu, Ming

doi:10.1109/access.2021.3049830

Cited by 21 publications

(11 citation statements)

References 29 publications

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“…Vasudeva and Sood [68] discussed potential mechanisms to prevent fake node and Sybil attacks that include symmetric cryptography using a central authority, random key Pham et al [50] Strong authentication methods. Li et al [69] Entropy theory for predicting traffic distribution. Chang et al [70] Location-hidden authorized message generation scheme.…”

Section: Defenses Against Fake Node and Sybil Attackmentioning

confidence: 99%

“…Pham et al [50] described strong authentication methods that can address impersonation or Sybil attacks preventing adversaries to send falsified information over the V2X communication channels to disrupt CAVs operation and traffic flow. Li et al [69] proposed a Real-Time Edge Detection Scheme for Sybil DDoS that used the entropy theory to quantify the traffic distribution and further designed an algorithm named Fast Quartile Deviation Check (FQDC) to recognize and locate the attack on the Internet of Vehicles (IoV). Footprint is a Sybil attack detection mechanism that used the trajectories of vehicles for identification without affecting the anonymity and location privacy of vehicles [70].…”

Section: Defenses Against Fake Node and Sybil Attackmentioning

confidence: 99%

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An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles

Gupta¹,

Maple²

2023

Preprint

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<p>Edge Computing-based Connected Autonomous Vehicles (EC-CAVs) can fulfill the emerging demand for smart transportation on a global scale. Such innovations to transportation ecosystems can bring manyfold benefits from fully autonomous driving services to proactive vehicle monitoring and traffic management. Given the complexity involved in the deployment of EC-CAVs, zero-tolerance safety and security measures must be incorporated to avert vehicle immobilization, road accidents, disclosure of sensitive data, or any potential threats. This paper presents a reference architecture of EC-CAVs to investigate existing and emerging cyber-threats for deriving a common attack taxonomy for the EC-CAVs ecosystem. Subsequently, a comprehensive survey of security mechanisms is presented that can ensure safe and secure transportation of passengers from one destination to another using an EC-CAVs ecosystem.</p>

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Section: Defenses Against Fake Node and Sybil Attackmentioning

confidence: 99%

Section: Defenses Against Fake Node and Sybil Attackmentioning

confidence: 99%

An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles

Gupta¹,

Maple²

2023

Preprint

View full text Add to dashboard Cite

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“…Obfuscation of false information is performed using many fake identifiers issued by a single attacker in the form of a set of real nodes. e attacker, by this process, can control the IoV network challenging the security, efficiency, and consistency of the system [37]. (xviii) Fuzzy attack: the attacker focuses on studying a vehicle's behavior for a certain period to change its pattern.…”

Section: Possible Attacks On Iovmentioning

confidence: 99%

Architecture, Protocols, and Security in IoV: Taxonomy, Analysis, Challenges, and Solutions

Karim

Habbal

Chaudhry

et al. 2022

Security and Communication Networks

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Internet of Vehicles (IoV) is a multinode network that exchanges information in an open, wireless environment. Various communication activities exist between IoV entities to share important information such as (ID, location, speed, messages, and traffic information), necessary for network operation. As part of intelligent transportation, IoV is considered a hot subject for researchers, because it is still facing many unresolved challenges, especially those concerning security and privacy. The variation of security-privacy threats that can menace the safety, privacy, and lives of vehicle occupants makes security the leading point of interest. The development of communication protocols for autonomous vehicles opens to us new issues to study and enhance the performance of IoV networks in terms of security and privacy. Several works have been reported, proposing many solutions for practical security challenges including a considerable number of survey-review papers published in respectable channels. The main motive of this review paper is to present the latest developments related to IoV security, as well as to address existing limitations. The high frequency of publication on IoV architecture, security, and new solutions leads us to write a compact, comprehensive, and up-to-date review. Inclusion criteria for selected papers include recent publications, number of citations, and impact of the research. In the present survey paper, the IoV architecture model is defined with all related communication types, and security and privacy issues are analyzed and presented with recently proposed solutions in a clear method. Clear classifications of threats, attacks, protocols, and solutions are presented. Moreover, the use of blockchain-based IoV to improve system security is discussed highlighting the most important trends and taxonomies. The paper was written to be a candidate as the first to read on the topic of the IoV security challenge, presenting problems and solutions in a clear, smooth, complete, and integrated manner.

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“…Deception is used for both defensive and offensive interactions. Researchers have developed various defences against offensive deception in connected vehicular networks such as Sybil attacks [33], false positioning attacks [34], illusion attacks [35] and topology poisoning attacks [36]. Honeypots have been widely implemented in network security for defensive deception [37], [38], moving target defence [39] and against advanced persistent threats [40].…”

Section: Related Workmentioning

confidence: 99%

HoneyCar: A Framework to Configure Honeypot Vulnerabilities on the Internet of Vehicles

et al. 2022

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The Internet of Vehicles (IoV), whereby interconnected vehicles that communicate with each other and with road infrastructure on a common network, has promising socio-economic benefits but also poses new cyber-physical threats. To protect these entities and learn about adversaries, data on attackers can be realistically gathered using decoy systems like honeypots. Admittedly, honeypots introduces a trade-off between the level of honeypot-attacker interactions and incurred overheads and costs for implementing and monitoring these systems. Deception through honeypots can be achieved by strategically configuring the honeypots to represent components of the IoV to engage attackers and collect cyber threat intelligence. Here, we present HoneyCar, a novel decision support framework for honeypot deception in IoV. HoneyCar benefits from the repository of known vulnerabilities of the autonomous and connected vehicles found in the Common Vulnerabilities and Exposure (CVE) database to compute optimal honeypot configuration strategies. The adversarial interaction is modelled as a repeated imperfect-information zero-sum game where the IoV network administrator strategically chooses a set of vulnerabilities to offer in a honeypot and a strategic attacker chooses a vulnerability to exploit under uncertainty. Our investigation examines two different versions of the game, with and without the re-configuration cost, to empower the network administrator to determine optimal honeypot investment strategies given a budget. We show the feasibility of this approach in a case study that consists of the vulnerabilities in autonomous and connected vehicles gathered from the CVE database and data extracted from the Common Vulnerability Scoring System (CVSS).

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RTED-SD: A Real-Time Edge Detection Scheme for Sybil DDoS in the Internet of Vehicles

Abstract: Date of publication xxxx, date of current version xxxx.

Cited by 21 publications

References 29 publications

An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles

An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles

Architecture, Protocols, and Security in IoV: Taxonomy, Analysis, Challenges, and Solutions

HoneyCar: A Framework to Configure Honeypot Vulnerabilities on the Internet of Vehicles

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