Enhanced Position Verification for VANETs Using Subjective Logic

Heijden, Rens W. van der; Al-Momani, Ala'a; Kargl, Frank; Abu-Sharkh, Osama M. F.

doi:10.1109/vtcfall.2016.7881000

Cited by 31 publications

(25 citation statements)

References 10 publications

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“…Misbehavior detection has been the subject of research for many years [28,22,29,26,[30][31][32]. Most of the existing detection mechanisms are data-centric which aims to detect the false messages through analyzing the consistency and plausibility of the information against predefined security thresholds.…”

Section: Related Workmentioning

confidence: 99%

“…Detecting false context information has been the focus of many researchers in the literature [28,22,29,26,[30][31][32]. Two main concepts have been used for detecting false information in VANET: plausibility and consistency concepts.…”

Section: Related Workmentioning

confidence: 99%

“…Misbehavior Detection System (MDS) acts as a second wall of defense when the prevention mechanisms are broken to ensure the quality of the data that are shared by neighboring vehicles. Misbehavior detection in VANET has been the subject of research for many years [28,22,29,26,[30][31][32]. Misbehavior detection solutions are categorized into two classes based on the detection target: Entity-Centric (EC) for detecting the misbehaving vehicles [28, 22,26] and Data-Centric for detecting the false information shared among vehicles [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

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Context-aware data-centric misbehaviour detection scheme for vehicular ad hoc networks using sequential analysis of the temporal and spatial correlation of the consistency between the cooperative awareness messages

Ghaleb

Maarof

Zainal

et al. 2019

Vehicular Communications

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Vehicular ad hoc Networks (VANETs) are emerged mainly to improve road safety, traffic efficiency, and passenger comfort. The performance of most VANET applications and services relies on the availability of accurate and recent mobility-information, so-called Cooperative Awareness Messages (CAM), shared by neighboring vehicles. However, misbehaving in terms of sharing false mobility information can disrupt any potential VANET application. Because cryptographic solutions in VANET are expensive, complicated, and vulnerable to internal misbehavior, security lapses are inevitable. Therefore, misbehavior detection is an important security component. Unfortunately, existing misbehavior detection solutions lack considering the high dynamicity of vehicular context which leads to low detection accuracy and high false alarms. The use of predefined and static security thresholds are the main drawbacks of the existing solutions. In this paper, a context-aware misbehavior detection scheme (CAMDS) is proposed using sequential analysis of temporal and spatial correlation of neighboring vehicles' mobility information. A dynamic context reference is constructed online and timely updated using statistical techniques. Firstly, the Kalman filter algorithm is used to track the mobility information received from neighboring vehicles. Then, the innovation errors of the Kalman filter are utilized to construct a temporal consistency assessment model for each neighboring vehicle using Box-plot. Then, the Hampel filter is used to construct a spatial consistency assessment model that represents the current context reference model. Similarly, plausibility assessment reference models are built online and timely updated using the Hampel filter and by utilizing the consistency assessment reference model of neighboring information. Finally, a message is classified as suspicious if its consistency and plausibility scores deviate much from the context reference model. The proposed context-aware scheme achieved a 73% reduction in false Alarm rate while it achieves a 37% improvement in the detection rate. This proves the effectiveness of the proposed scheme compared with the existing static solutions.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Context-aware data-centric misbehaviour detection scheme for vehicular ad hoc networks using sequential analysis of the temporal and spatial correlation of the consistency between the cooperative awareness messages

Ghaleb

Maarof

Zainal

et al. 2019

Vehicular Communications

View full text Add to dashboard Cite

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“…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%

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

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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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“…A malicious vehicle can broadcast false position information in the network that has adverse consequences in safety applications [15][16][17][18][19][20]. VANETs' routing, safety application, traffic management and data aggregation rely on correct vehicle position information [21,22].…”

mentioning

confidence: 99%

A survey of local/cooperative-based malicious information detection techniques in VANETs

Arshad

Ullah

Ahmad

et al. 2018

J Wireless Com Network

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Vehicular ad hoc networks (VANETs) are emerged technology where vehicles and roadside units (RSUs) communicate with each other. VANETs can be categorized as a subbranch of mobile ad hoc networks (MANETs). VANETs help to improve traffic efficiency and safety and provide infotainment facility as well. The dissemination of messages must be relayed through nodes in VANETs. However, it is possible that a node may propagate false information in a network due to its malicious behaviour or selfishness. False information in VANETs can change drivers' behaviour and create disastrous consequences in the network. Therefore, sometimes false safety messages may endanger human life. To avoid any lass, it is more important to detect and avoid false messages. This paper has explained some important algorithms that can detect false messages in VANETs. The categorization of false message detection schemes based on local and cooperative behaviour has been presented in this article. The limitations and consequences of existing schemes as well as future work has been discussed. Keywords: VANETs, MANETs, Misbehaviour, False message detection, Security ReviewThis article analyses Single/local and Cooperative based malicious information detection techniques in VANETs. Single/local based detection schemes are further comprised of plausibility, consistency and single node behavior. However, these techniques performances are not upto the mark, because of single node reliance. The cooperative based detection techniques are more efficient than Single/local based detection schemes. The prerequisites for these scheme need more nodes, unlike Single/local based detection schemes. These techniques are classified based on consistency and behavior with neighbors. Trust-based detection techniques are analysed on previous communication history. However, these scheme needs a honest and sufficient number of nodes for reliable result.

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Enhanced Position Verification for VANETs Using Subjective Logic

Cited by 31 publications

References 10 publications

Context-aware data-centric misbehaviour detection scheme for vehicular ad hoc networks using sequential analysis of the temporal and spatial correlation of the consistency between the cooperative awareness messages

Context-aware data-centric misbehaviour detection scheme for vehicular ad hoc networks using sequential analysis of the temporal and spatial correlation of the consistency between the cooperative awareness messages

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

A survey of local/cooperative-based malicious information detection techniques in VANETs

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