MARV-X: Applying Maneuver Assessment for Reliable Verification of Car-to-X Mobility Data

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

doi:10.1109/tits.2013.2262176

Cited by 12 publications

(26 citation statements)

References 16 publications

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“…The proposed CA-DC-MDM is compared with the scheme proposed by Stübing, et al, [53]. This scheme consists of many plausibility and consistency models which was developed and improved by Firl, et al, [46], Jaeger, et al, [37], Leinmüller, et al, [48], Schmidt, et al, [50]. It is common to evaluate the data-centric misbehaviour detection using the normal dataset (before simulating the attacker actions) in terms of false positive rate (FBR) in order to evaluate its effectiveness under different communication scenarios [53,46,22].…”

Section: Resultsmentioning

confidence: 99%

“…Misbehaving vehicles which share false context information through manipulating their own mobility information to resemble a traffic pattern event, such as hard braking, crash pattern, or congestion pattern can create illusion and cause neighbouring vehicles to accept false information [40][41][42][43][44]. Contextinformation-based misbehaviour detection has many advantages over event-information-based misbehaviour detection because it can detect the attacks in their initial stages before [27,45,46,38]. Manipulating context information can trigger the applications of benign vehicles to spread false information such as Electronic Emergency Brake Light (EEBL), Slow and Suddenly Stop Vehicle message (S&SV), Post-Crash Notification (PCN), Congestion Message (CM) among many others.…”

Section: Related Workmentioning

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: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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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“…Therefore, it is commonly believed that context-based data-centric misbehavior detection has many advantages. For example, context-based misbehavior detection can detect most types of attacks that send false information, including false events [29,44,49,50]. Most of the possible attacks in VANET are performed through manipulating context information [51,52].…”

Section: Related Workmentioning

confidence: 99%

“…Three types of multifaceted and hybrid features were used to construct the context reference, namely, the consistency-based, plausibility-based, and behavioral-based features. The Kalman filter-based algorithm was used to periodically track and predict the mobility states of the neighboring vehicles due to its efficiency in tracking multiple vehicles' states that are suitable for real-time requirements of VANET applications [29]. During such tracking, the Kalman filter-based algorithm collects recent mobility information of the neighboring vehicles.…”

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confidence: 99%

Ensemble-Based Hybrid Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network

et al. 2019

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Life-saving decisions in vehicular ad hoc networks (VANETs) depend on the availability of highly accurate, up-to-date, and reliable data exchanged by neighboring vehicles. However, spreading inaccurate, unreliable, and false data by intruders create traffic illusions that may cause loss of lives and assets. Although several solutions for misbehavior detection have been proposed to address these issues, those solutions lack adequate representation and the adaptability to vehicular context. The use of predefined static thresholds and lack of comprehensive context representation have rendered the existing solutions limited to specific scenarios and attack types, which impedes their generalizability. This paper addresses these limitations by proposing an ensemble-based hybrid context-aware misbehavior detection system (EHCA-MDS) model. EHCA-MDS has been developed in four phases, as follows. The static thresholds have been replaced by dynamic ones created on the fly by analyzing the spatial and temporal properties of the mobility information collected from neighboring vehicles. Kalman filter-based algorithms were used to collect the mobility information of neighboring vehicles. Three sets of features were then derived, each of which has a different perspective, namely data consistency, data plausibility, and vehicle behavior. These features were used to construct a dynamic context reference using the Hampel filter. The Hampel-based z-score was used to evaluate the vehicles based on their behavioral activities, data consistency, and plausibility. For comprehensive features representation, multifaceted, non-parametric-based statistical classifiers were constructed and updated online using a Hampel filter-based algorithm. For accurate representation, the output of the statistical classifiers, vehicles' scores, context reference parameters, and the derived features were used as input to an ensemble learning-based algorithm. Such representation helps to identify the misbehaving vehicles more effectively. The proposed EHCA-MDS model was evaluated in the presence of different types of misbehaving vehicles under different context scenarios through extensive simulations, utilizing a real-world traffic dataset. The results show that the accuracy and robustness of the proposed EHCA-MDS under different vehicular dynamic context scenarios were higher than existing solutions, which confirms its feasibility and effectiveness to improve the performance of VANET critical applications. IntroductionRoad collisions are increasing, and they are being expected to be the fifth leading cause of death by 2030 [1,2]. Annually, millions of people lose their lives on roads worldwide due to traffic accidents [1], with 40 times more suffering from injuries. These accidents are also the main cause of traffic congestion, which in turn has a great impact on the economy [3,4], and billions of dollars are lost due to the treatment of injuries, loss of property, lost working hours, and high fuel consumption [5]. Several studies reveal that more than 95%...

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“…This kind of method is suitable for verification of static claimant only. The use of probabilistic framework for verification of car‐to‐ X mobility data can be an effective approach, but there is always a risk of high false positives, which is an undesirable situation in VCPS .…”

Section: Related Workmentioning

confidence: 99%

KITE: an efficient scheme for trust estimation and detection of errant nodes in vehicular cyber-physical systems

Barnwal¹,

Ghosh

2016

Security Comm. Networks

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Advancement in sensing and networking technology enhances the capability of mechatronic systems by many folds and improves the way it interacts with its physical environment. However, the dependency on network technology introduces a new set of challenges. Vehicular cyber‐physical system (VCPS) is one such example that came into existence with the objective of enhancing road safety and convenience. In VCPS, the safety applications rely on the correctness of the kinematics information received from other vehicles in the vicinity. Incorrect kinematics information may result in increase in road hazards and creates more confusion than convenience. Irrespective of the reasons, detection of such errant nodes in VCPS is imperative. Moreover, the trust estimation of the nodes can play an important role to classify them as genuine and errant. In this work, an efficient scheme is proposed for trust estimation and detection of those errant nodes that disseminate incorrect kinematics information in VCPS. Simulations using ns‐2 and VanetMobiSim under realistic vehicular network environment are conducted to evaluate the performance of the proposed scheme. The results are encouraging and reveal that the proposed scheme is suitable for implementation in real vehicular environment for detection of such errant nodes with high accuracy. Copyright © 2016 John Wiley & Sons, Ltd.

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MARV-X: Applying Maneuver Assessment for Reliable Verification of Car-to-X Mobility Data

Cited by 12 publications

References 16 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

Ensemble-Based Hybrid Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network

KITE: an efficient scheme for trust estimation and detection of errant nodes in vehicular cyber-physical systems

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