Secure Privacy-Preserving V2V Communication in 5G-V2X Supporting Network Slicing

Mun, Hyeran; Seo, Minhye; Lee, Dong Hoon

doi:10.1109/tits.2021.3129484

Cited by 17 publications

(4 citation statements)

References 39 publications

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“…The UAV has a complex architecture and most of the information regarding measures in place is confidential, so it is difficult to identify which threat is more dangerous to a UAV that needs to be modelled at utmost priority [106]. Moreover, there has been wide research for modelling the security and privacy of vehicleto-vehicle (V2V) [117], Vehicle-to-infrastructure (V2I) [118] and Vehicle-to-everything (V2X) [119]. But threat modelling techniques (such as STRIDE, LINDDUN) are not sufficient for securing V2V, V2I and V2X because they require the autonomy approach [120] to deal with privacy and security vulnerabilities.…”

Section: Challenges and Difficulties In Conducting Threat Modelling F...mentioning

confidence: 99%

Data Privacy Threat Modelling for Autonomous Systems: A Survey From the GDPR's Perspective

Azam

Michala

Ansari

et al. 2023

IEEE Trans. Big Data

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Intelligence-based applications have been increasingly deployed in every field of life including smart homes, smart cities, healthcare services, and autonomous systems where personal data is collected across heterogeneous sources and processed using "black-box" algorithms in opaque centralised servers. As a consequence, preserving the data privacy and security of these applications is of utmost importance. In this respect, a modelling technique for identifying potential data privacy threats and specifying countermeasures to mitigate the related vulnerabilities in such AI-based systems plays a significant role in preserving and securing personal data. Various threat modelling techniques have been proposed such as STRIDE, LINDDUN, and PASTA but none of them is sufficient to model the data privacy threats in autonomous systems. Furthermore, they are not designed to model compliance with data protection legislation like the EU/UK General Data Protection Regulation (GDPR), which is fundamental to protecting data owners' privacy as well as to preventing personal data from potential privacy-related attacks. In this article, we survey the existing threat modelling techniques for data privacy threats in autonomous systems and then analyse such techniques from the viewpoint of GDPR compliance. Following the analysis, We employ STRIDE and LINDDUN in autonomous cars, a specific use-case of autonomous systems, to scrutinise the challenges and gaps of the existing techniques when modelling data privacy threats. Prospective research directions for refining data privacy threats & GDPR-compliance modelling techniques for autonomous systems are also presented.

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Section: Challenges and Difficulties In Conducting Threat Modelling F...mentioning

confidence: 99%

Data Privacy Threat Modelling for Autonomous Systems: A Survey From the GDPR's Perspective

Azam

Michala

Ansari

et al. 2023

IEEE Trans. Big Data

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show abstract

“…However, in such a heterogeneous network, the risks of vehicle operation safety, data privacy and network security complicate the security of V2X communication. Network security is the basic premise for ensuring the operational safety of vehicles and an important fulcrum for realizing data privacy, making network security a top priority for ensuring V2X security [4]. Unfortunately, the ultra-large scale of the 6G V2X network further expands the attack surface of traditional mobile networks.…”

Section: Introductionmentioning

confidence: 99%

Access-Side DDoS Defense for Space-Air-Ground Integrated 6G V2X Networks

Chen,

Feng,

Chen

et al. 2024

IEEE Open J. Commun. Soc.

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Distributed Denial of Service (DDoS) attacks pose a significant threat to the Internet of Vehicles, causing delays in driving operations and risking personal safety. With the emerging of Space-Air-Ground Integrated Sixth-Generation (6G) networks, DDoS defense faces new challenges, especially in open and complex Vehicle-to-Everything (V2X) connections. In this study, we reviewed the requisites and challenges of DDoS defense in V2X and introduced a tailored access-side defense architecture for Space-Air-Ground Integrated 6G V2X. The objective is to provide ultra-low latency and privacy-assured services despite limited communication resources. Our architecture uses access-side control points (CPs) for rapid attack responses, collaborating with local controllers to create a seamless defense perimeter at the network edge. This prevents malicious traffic infiltration. Incorporating proactive defense strategies and lightweight detection methods, our approach ensures high attack detection rates with low defense costs. Simulation results confirm its efficacy and advantages in defense and network efficiency. We also discuss open issues for future research to facilitate practical applications.INDEX TERMS access-side defense architecture, distributed denial of service (DDoS), defense efficiency, sixth generation (6G), vehicle-to-everything (V2X).

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“…The situation gets worse with the introduction of network slicing. Indeed, combining 5G-V2X with NS will increase attack severity and open up a network slicing attack vector to CAVs [3]. We can classify these attacks into two main categories: (i) intra-slice attacks in which the attacker(s) and the target(s) belong to the same V2X Network Slice (V2X-NS), and (ii) inter-slice attacks in which the attacker(s) and/or the target(s) belong to different V2X-NSs.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-based Intra-slice Attack Detection for 5G-V2X Sliced Networks

Boualouache

Djaidja

Senouci

et al. 2022

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

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Connected and Automated Vehicles (CAVs) represent one of the main verticals of 5G to provide road safety, road traffic efficiency, and user convenience. As a key enabler of 5G, Network Slicing (NS) aims to create Vehicle-to-Everything (V2X) network slices with different network requirements on a shared and programmable physical infrastructure. However, NS has generated new network threats that might target CAVs leading to road hazards. More specifically, such attacks may target either the inner functioning of each V2X-NS (intra-slice) or break the NS isolation. In this paper, we aim to deal with the raised question of how to detect intra-slice V2X attacks. To do so, we leverage both Virtual Security as a Service (VSaS) concept and deep learning (DL) to deploy a set of DL-empowered security Virtual Network Functions (sVNFs) within V2X-NSs. These sVNFs are in charge of detecting such attacks, thanks to a DL model that we also build in this work. The proposed DL model is trained, validated, and tested using a publicly available dataset. The results show the efficiency and accuracy of our scheme to detect intra-slice V2X attacks.

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Secure Privacy-Preserving V2V Communication in 5G-V2X Supporting Network Slicing

Cited by 17 publications

References 39 publications

Data Privacy Threat Modelling for Autonomous Systems: A Survey From the GDPR's Perspective

Data Privacy Threat Modelling for Autonomous Systems: A Survey From the GDPR's Perspective

Access-Side DDoS Defense for Space-Air-Ground Integrated 6G V2X Networks

Deep Learning-based Intra-slice Attack Detection for 5G-V2X Sliced Networks

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