1,088 research outputs found
MARINE: Man-in-the-middle attack resistant trust model IN connEcted vehicles
Vehicular Ad-hoc NETwork (VANET), a novel technology holds a paramount importance within the transportation domain due to its abilities to increase traffic efficiency and safety. Connected vehicles propagate sensitive information which must be shared with the neighbors in a secure environment. However, VANET may also include dishonest nodes such as Man-in-the-Middle (MiTM) attackers aiming to distribute and share malicious content with the vehicles, thus polluting the network with compromised information. In this regard, establishing trust among connected vehicles can increase security as every participating vehicle will generate and propagate authentic, accurate and trusted content within the network. In this paper, we propose a novel trust model, namely, Man-in-the-middle Attack Resistance trust model IN connEcted vehicles (MARINE), which identifies dishonest nodes performing MiTM attacks in an efficient way as well as revokes their credentials. Every node running MARINE system first establishes trust for the sender by performing multi-dimensional plausibility checks. Once the receiver verifies the trustworthiness of the sender, the received data is then evaluated both directly and indirectly. Extensive simulations are carried out to evaluate the performance and accuracy of MARINE rigorously across three MiTM attacker models and the bench-marked trust model. Simulation results show that for a network containing 35% MiTM attackers, MARINE outperforms the state of the art trust model by 15%, 18%, and 17% improvements in precision, recall and F-score, respectively.N/A
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MobileTrust: Secure Knowledge Integration in VANETs
Vehicular Ad hoc NETworks (VANET) are becoming popular due to the emergence of the Internet of Things and ambient intelligence applications. In such networks, secure resource sharing functionality is accomplished by incorporating trust schemes. Current solutions adopt peer-to-peer technologies that can cover the large operational area. However, these systems fail to capture some inherent properties of VANETs, such as fast and ephemeral interaction, making robust trust evaluation of crowdsourcing challenging. In this article, we propose MobileTrust—a hybrid trust-based system for secure resource sharing in VANETs. The proposal is a breakthrough in centralized trust computing that utilizes cloud and upcoming 5G technologies to provide robust trust establishment with global scalability. The ad hoc communication is energy-efficient and protects the system against threats that are not countered by the current settings. To evaluate its performance and effectiveness, MobileTrust is modelled in the SUMO simulator and tested on the traffic features of the small-size German city of Eichstatt. Similar schemes are implemented in the same platform to provide a fair comparison. Moreover, MobileTrust is deployed on a typical embedded system platform and applied on a real smart car installation for monitoring traffic and road-state parameters of an urban application. The proposed system is developed under the EU-founded THREAT-ARREST project, to provide security, privacy, and trust in an intelligent and energy-aware transportation scenario, bringing closer the vision of sustainable circular economy
Data-centric Misbehavior Detection in VANETs
Detecting misbehavior (such as transmissions of false information) in
vehicular ad hoc networks (VANETs) is very important problem with wide range of
implications including safety related and congestion avoidance applications. We
discuss several limitations of existing misbehavior detection schemes (MDS)
designed for VANETs. Most MDS are concerned with detection of malicious nodes.
In most situations, vehicles would send wrong information because of selfish
reasons of their owners, e.g. for gaining access to a particular lane. Because
of this (\emph{rational behavior}), it is more important to detect false
information than to identify misbehaving nodes. We introduce the concept of
data-centric misbehavior detection and propose algorithms which detect false
alert messages and misbehaving nodes by observing their actions after sending
out the alert messages. With the data-centric MDS, each node can independently
decide whether an information received is correct or false. The decision is
based on the consistency of recent messages and new alert with reported and
estimated vehicle positions. No voting or majority decisions is needed, making
our MDS resilient to Sybil attacks. Instead of revoking all the secret
credentials of misbehaving nodes, as done in most schemes, we impose fines on
misbehaving nodes (administered by the certification authority), discouraging
them to act selfishly. This reduces the computation and communication costs
involved in revoking all the secret credentials of misbehaving nodes.Comment: 12 page
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