A two-stage verification process for Car-to-X mobility data based on path prediction and probabilistic maneuver recognition

Stübing, Hagen; Firl, Jonas; Huss, Sorin A.

doi:10.1109/vnc.2011.6117119

Cited by 29 publications

(27 citation statements)

References 5 publications

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“…Stübing et al [4] have proposed a different approach; rather than developing a generic framework for misbehavior detection, they have developed a framework to combine several information sources that are all concerned with correctness of position and movement information. Specifically, their approach combines several autonomous data-centric mechanisms (path prediction and maneuver recognition), which allow them to accurately predict the movement of neighboring vehicles.…”

Section: B Frameworkmentioning

confidence: 99%

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Enhanced Position Verification for VANETs Using Subjective Logic

Heijden

Al-Momani

Kargl

et al. 2016

2016 IEEE 84th Vehicular Technology Conference (VTC-Fall)

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The integrity of messages in vehicular ad-hoc networks has been extensively studied by the research community, resulting in the IEEE 1609.2 standard, which provides typical integrity guarantees. However, the correctness of message contents is still one of the main challenges of applying dependable and secure vehicular ad-hoc networks. One important use case is the validity of position information contained in messages: position verification mechanisms have been proposed in the literature to provide this functionality. A more general approach to validate such information is by applying misbehavior detection mechanisms. In this paper, we consider misbehavior detection by enhancing two position verification mechanisms and fusing their results in a generalized framework using subjective logic. We conduct extensive simulations using VEINS to study the impact of traffic density, as well as several types of attackers and fractions of attackers on our mechanisms. The obtained results show the proposed framework can validate position information as effectively as existing approaches in the literature, without tailoring the framework specifically for this use case.

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Section: B Frameworkmentioning

confidence: 99%

“…Previous authors have studied data correctness in VANETs. The authors of [3], [4] concentrated specifically on position information while the authors of [5], [6] have taken the more general approach of misbehavior detection. Misbehavior detection can be categorized as data-centric and nodecentric [7].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Position Verification for VANETs Using Subjective Logic

Heijden

Al-Momani

Kargl

et al. 2016

2016 IEEE 84th Vehicular Technology Conference (VTC-Fall)

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“…In this work, we compare four: the acceptance range threshold (ART), the sudden appearance warning (SAW), the simple speed check (SSC), and the distance moved verifier (DMV). Of these, the acceptance range threshold is the most well-studied, originally introduced by Leinmüller et al [10] and later used by others, including Stübing et al [18] and in our earlier work [19]. It basically uses the expected reception range as a measure for the plausibility of the position included in incoming single-hop beacon messages, which are the most important source of information for VANET applications.…”

Section: Evaluation Of Plausibility Detectorsmentioning

confidence: 99%

“…The simple speed check decides maliciousness based on how the claimed speed relates to the speed implied by the position and time differences between the current and the previous beacon, and the claimed speed in the current beacon. If the deviation exceeds a threshold, this detector classifies the message as malicious (similar to, but much simpler than, a Kalman filter [18]). Finally, the distance moved verifier checks whether the vehicle moved a minimum distance (similar to the way the MDM proposed by Schmidt et al [16]), and if this distance is too small, the message is considered malicious.…”

Section: Evaluation Of Plausibility Detectorsmentioning

confidence: 99%

“…For example, similar to the area of intrusion detection, it is intuitively obvious that it is hard to build a single detection mechanism that detects all possible attacks. Instead, many proposals aim to either detect specific attacks [19] (i.e., particular types of behavior that are malicious), or they try to protect a specific application by structuring the checks such that only correct messages are accepted [18]. Many authors have proposed to apply some type of data fusion as a tool to combine information from multiple sources [5,13,19], but it is not well-studied how individual detection mechanisms compare.…”

Section: Introductionmentioning

confidence: 99%

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VeReMi: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs

Heijden

Lukaseder

Kargl

2018

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

133

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Vehicular networks are networks of communicating vehicles, a major enabling technology for future cooperative and autonomous driving technologies. The most important messages in these networks are broadcast-authenticated periodic one-hop beacons, used for safety and traffic efficiency applications such as collision avoidance and traffic jam detection. However, broadcast authenticity is not sufficient to guarantee message correctness. The goal of misbehavior detection is to analyze application data and knowledge about physical processes in these cyber-physical systems to detect incorrect messages, enabling local revocation of vehicles transmitting malicious messages. Comparative studies between detection mechanisms are rare due to the lack of a reference dataset. We take the first steps to address this challenge by introducing the Vehicular Reference Misbehavior Dataset (VeReMi) and a discussion of valid metrics for such an assessment. VeReMi is the first public extensible dataset, allowing anyone to reproduce the generation process, as well as contribute attacks and use the data to compare new detection mechanisms against existing ones. The result of our analysis shows that the acceptance range threshold and the simple speed check are complementary mechanisms that detect different attacks. This supports the intuitive notion that fusion can lead to better results with data, and we suggest that future work should focus on effective fusion with VeReMi as an evaluation baseline.

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