Hindering false event dissemination in VANETs with proof-of-work mechanisms

Palomar, Esther; Fuentes, José María de; González-Tablas, Ana I.; Alcaide, Almudena

doi:10.1016/j.trc.2011.08.002

Cited by 18 publications

(7 citation statements)

References 10 publications

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“…Attacks on availability are especially dangerous for ITS due to the real-time operational requirements of many ITS components. Some of the existing solutions against denial-of-service attack include signature-based authentication [14] and proof-of-work [64] as shown in Table 3.…”

Section: Availabilitymentioning

confidence: 99%

Security and Privacy Issues in Intelligent Transportation Systems: Classification and Challenges

Hahn

Munir

Behzadan

2021

IEEE Intell. Transport. Syst. Mag.

109

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Intelligent Transportation Systems (ITS) aim at integrating sensing, control, analysis, and communication technologies into travel infrastructure and transportation to improve mobility, comfort, safety, and efficiency. Car manufacturers are continuously creating smarter vehicles, and advancements in roadways and infrastructure are changing the feel of travel. Traveling is becoming more efficient and reliable with a range of novel technologies, and research and development in ITS. Safer vehicles are introduced every year with greater considerations for passenger and pedestrian safety, nevertheless, the new technology and increasing connectivity in ITS present unique attack vectors for malicious actors. Smart cities with connected public transportation systems introduce new privacy concerns with the data collected about passengers and their travel habits. In this paper, we provide a comprehensive classification of security and privacy vulnerabilities in ITS. Furthermore, we discuss challenges in addressing security and privacy issues in ITS and contemplate potential mitigation techniques. Finally, we highlight future research directions to make ITS more safe, secure, and privacy-preserving.

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Section: Availabilitymentioning

confidence: 99%

Security and Privacy Issues in Intelligent Transportation Systems: Classification and Challenges

Hahn

Munir

Behzadan

2021

IEEE Intell. Transport. Syst. Mag.

109

View full text Add to dashboard Cite

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“…This works because the computation becomes exponentially harder and cannot be precomputed. One proposal to use PoW mechanisms for cITS is [119], which requires PoW for evidence that an event actually occurred. The problem with these approaches is that they pose significant computational overhead, and thus cannot work in all situations.…”

Section: B Open Issuesmentioning

confidence: 99%

Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systems

Heijden

Dietzel

Leinmüller

et al. 2019

IEEE Commun. Surv. Tutorials

195

148

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Cooperative Intelligent Transportation Systems (cITS) are a promising technology to enhance driving safety and efficiency. Vehicles communicate wirelessly with other vehicles and infrastructure, thereby creating a highly dynamic and heterogeneously managed ad-hoc network. It is these network properties that make it a challenging task to protect integrity of the data and guarantee its correctness. A major component is the problem that traditional security mechanisms like PKIbased asymmetric cryptography only exclude outsider attackers that do not possess key material. However, because attackers can be insiders within the network (i.e., possess valid key material), this approach cannot detect all possible attacks. In this survey, we present misbehavior detection mechanisms that can detect such insider attacks based on attacker behavior and information analysis. In contrast to well-known intrusion detection for classical IT systems, these misbehavior detection mechanisms analyze information semantics to detect attacks, which aligns better with highly application-tailored communication protocols foreseen for cITS. In our survey, we provide an extensive introduction to the cITS ecosystem and discuss shortcomings of PKI-based security. We derive and discuss a classification for misbehavior detection mechanisms, provide an in-depth overview of seminal papers on the topic, and highlight open issues and possible future research trends.

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“…Most of the possible attacks in VANET are performed through manipulating context information [51,52]. In addition, attackers in VANET target this type of information as they can inflict major damage easily while they remain undetected [23,25,26,53]. As such, this study aims to build a mobility information-based MDS.…”

Section: Related Workmentioning

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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Hindering false event dissemination in VANETs with proof-of-work mechanisms

Cited by 18 publications

References 10 publications

Security and Privacy Issues in Intelligent Transportation Systems: Classification and Challenges

Security and Privacy Issues in Intelligent Transportation Systems: Classification and Challenges

Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systems

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

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