A Misbehavior Authority System for Sybil Attack Detection in C-ITS

Kamel, Joseph; Haidar, Farah; Jemaa, Inès Ben; Kaiser, Arnaud; Lonc, Brigitte; Urien, Pascal

doi:10.1109/uemcon47517.2019.8993045

Cited by 32 publications

(18 citation statements)

References 11 publications

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“…Hardware/Software-based Attacks Several attacks can take place over the hardware components and software sys- Platoon Attack [37] DDoS Attack [38] DDoS Attack [74] DDoS Attack [77] DDoS Attack [39] GreyHole & BlackHole [40] Black hole [41] Sybil Attack [42] Sybil Attack [43] Sybil Attack [44] Jamming Attack [69] Jamming Attack [45] jamming Attack [91] Data Manipulation [70] Crossfire Attack [56] Spoofing Attack [57] Spoofing Attack [58] Spoofing Attack [92] Cyber Physical Attack [93] Cyber Physical Attack [46] MDS [47] MDS [49] MDS [50] MDS [82] MDS [68] MDS [76] MDS [55] MDS [48] FDI [51] IDS [52] IDS [59] IDS [60] IDS [53] IDS [54] IDS [81] IDS [73] IDS [94] IDS [95] Trust Computation [96] Trust Computation [61] Trust Computation [62] Trust Computation [63] Trust Computation [71] Trust Computation [65] Trust Computation [66] Trust Com...…”

Section: Security Attacks and Requirementsmentioning

confidence: 99%

“…Confidentiality [53] Availability [37]- [40], [44], [45], [49]- [55], [59], [60], [69], [70], [74], [77], [81], [84], [94], [98] Integrity [36], [46], [48], [52], [68], [76], [82], [91]- [93] Privacy [42], [51], [54], [64], [72], [75], [78], [80] Authentication [41]- [43], [46], [49], [53], [55]- [60] Trust [61]- [63], [65]- [67], [71], [79], [95], [96] This survey centers around the use of ML in achieving the above security requirements. As shown in Fig.…”

Section: Security Attacks and Requirementsmentioning

confidence: 99%

“…Privacy Protection [64], [72], [75], [78], [80] Trust Computation [61]- [63], [65]- [67], [71], [79], [95], [96] Misbehaviour or Intrusion Detection [46]- [55], [59], [60], [68], [73], [81], [82], [94], [136] Attack Detection Miscellaneous [70], [91]- [93] Spoofing Attack [56]- [58] Jamming Attack [44], [45], [69], [98] Sybil Attack [41]- [43] Black-Hole and Grey-Hole Attack [39], [40] DDoS [37], [38], [74], [77] Platoon Attack [36] Driver Identification/Fingerprinting [125], [137]- [151] Fig. 11.…”

Section: Security Solutionsmentioning

confidence: 99%

“…In this work, the authors use the driving pattern and eigen values of DPM to detect an attacker with an accurate classification rate of 100%. Kamel et al [43] present a generic RNN-based solution to perform global detection of sybil attacks. This work considers four different effects of a sybil attack which results in traffic congestion, data replay, DoS random, and DoS disruptive.…”

Section: B Attack Detectionmentioning

confidence: 99%

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Machine Learning for Security in Vehicular Networks: A Comprehensive Survey

Talpur,

Gurusamy

2021

Preprint

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Machine Learning (ML) has emerged as an attractive and viable technique to provide effective solutions for a wide range of application domains. An important application domain is vehicular networks wherein ML-based approaches are found to be very useful to address various problems. The use of wireless communication between vehicular nodes and/or infrastructure makes it vulnerable to different types of attacks. In this regard, ML and its variants are gaining popularity to detect attacks and deal with different kinds of security issues in vehicular communication. In this paper, we present a comprehensive survey of ML-based techniques for different security issues in vehicular networks. We first briefly introduce the basics of vehicular networks and different types of communications. Apart from the traditional vehicular networks, we also consider modern vehicular network architectures. We propose a taxonomy of security attacks in vehicular networks and discuss various security challenges and requirements. We classify the ML techniques developed in the literature according to their use in vehicular network applications. We explain the solution approaches and working principles of these ML techniques in addressing various security challenges and provide insightful discussion. The limitations and challenges in using ML-based methods in vehicular networks are discussed. Finally, we present observations and lessons learned before we conclude our work.

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Section: Security Attacks and Requirementsmentioning

confidence: 99%

Section: Security Attacks and Requirementsmentioning

confidence: 99%

Section: Security Solutionsmentioning

confidence: 99%

Section: B Attack Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning for Security in Vehicular Networks: A Comprehensive Survey

Talpur,

Gurusamy

2021

Preprint

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“…Here, we briefly summarize the general purpose of the implemented checks. In contrast, a more detailed and technical description could be found in our previous studies [9] [3]. Additionally, the implementation is open source and could be found on GitHub [10].…”

Section: Local Detectionmentioning

confidence: 99%

DARE: A Reports Dataset for Global Misbehavior Authority Evaluation in C-ITS

Haidar

Kamel

Jemaa

et al. 2020

2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)

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European and North American governments are actively working on improving road safety and traffic efficiency. To this end, their corresponding standardization bodies: ETSI and IEEE are developing the Cooperative Intelligent Transport Systems (C-ITS). In this system, vehicles and road side units communicate in order to enable new services and propose cooperative safety applications. However, the system is vulnerable to new types of threats if not adequately secured. The security and privacy protection is crucial to the user acceptance of such new system. Currently, the ETSI and IEEE proposed using a specific vehicular Public Key Infrastructure (PKI) to protect the C-ITS system. The PKI can protect the system against external attackers but it still vulnerable to internal attacks. Registered vehicles with valid certificates can still disturb the system by misusing its applications. The aim of misbehavior detection is to detect and mitigate the effect of internal attackers. The current misbehavior detection architecture includes a local embedded component and a cloud component. In this paper, we propose a misbehavior reports dataset of derived from the local embedded detection of misbehaving entities. This dataset can be used to further develop and evaluate the cloud component. The set includes different road topology, varying attacker penetration rates and attack scenarios.

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Secure and lightweight message dissemination framework for internet of vehicles

Bodkhe,

Tanwar

2024

Security and Privacy

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The Internet of Vehicles (IoV) revolutionizes vehicle communication in dynamic networks. Message dissemination in IoV involves sharing critical information for the safety and convenience of the IoV network. It is very crucial to secure message dissemination due to potential cyber‐attacks, traffic disruptions, and privacy breaches. Data integrity, authentication, and privacy are vital to maintaining trust and safety in the IoV network. This network consists of resource‐constrained IoV devices with limited resources due to the availability of embedded components in vehicular systems. Therefore, optimizing algorithms and protocols is crucial for efficient vehicle‐to‐everything (V2X) communication, enhancing safety and transportation efficiency. Solutions often include lightweight protocols and secure message exchange. This paper proposes a machine learning (ML) based secure and lightweight message dissemination framework for resource‐constrained IoV. First, we present an ML‐based threat classification model capable of effectively categorizing adversarial and nonadversarial data streams and delivering an optimized model with superior accuracy. Furthermore, we also propose a secure message dissemination scheme using lightweight cryptographic primitives, which significantly reduces computational, communication, and energy overhead. To validate the robustness of our proposed ML‐based secure and lightweight message dissemination framework, we evaluate it using various security parameters and performance measures such as computation cost, communication cost, energy cost, accuracy, precision, recall, and F1‐score. Our contributions promise to significantly enhance the security and efficiency of message dissemination in IoV environments and advance lightweight, secure, and reliable transportation systems for the future.

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A Misbehavior Authority System for Sybil Attack Detection in C-ITS

Cited by 32 publications

References 11 publications

Machine Learning for Security in Vehicular Networks: A Comprehensive Survey

Machine Learning for Security in Vehicular Networks: A Comprehensive Survey

DARE: A Reports Dataset for Global Misbehavior Authority Evaluation in C-ITS

Secure and lightweight message dissemination framework for internet of vehicles

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