The integrity of messages in vehicular ad-hoc networks has been extensively studied by the research community, resulting in the IEEE&#126;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.
Raya et al. @cite_8 have described a framework to combine various data-centric detection mechanisms. However, unlike our approach, their main focus is computing node trustworthiness, which is then used to evaluate the actual belief in the received message. This approach is fundamentally based on trust evaluation, which can be done using different logic frameworks, such as Dempster-Shafer theory or Bayesian inference. One of their results is that when uncertainty is high, Dempster-Shafer theory performs well; we use subjective logic in our work, which is an improvement over Dempster-Shafer theory @cite_6 . We also go beyond their work conceptually, building on earlier work by Dietzel et al. @cite_9 , and represent detection results in the logic framework, rather than just trust between nodes. This makes our work more flexible: it can conceptually represent aggregated information and can be tuned depending on network parameters, as discussed in earlier work @cite_10 .
@cite_9 @cite_10 @cite_6 @cite_8
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Vehicle to Vehicle (V2V) and Vehicle to Infrastruc- ture (V2I) communication aims to increase safety, efficiency, and comfort of drivers. Vehicles periodically broadcast their current status, such as position, velocity, and other information. Received information is stored in a local knowledge base, often called world model, and used for application decisions. Because of the potential impact, V2V communication is an interesting target for malicious attackers. Message integrity protection using cryptographic signatures only protects against outsider attackers. In addition to signatures, misbehavior detection mechanisms comparable to intrusion detection systems (IDS) are needed to detect insider attackers. Given the complexity and large number of foreseen V2V and V2I applications, misbehavior detection cannot be a one-size-fits-all solution. In this paper, we present a flexible framework that can combine a range of different misbehavior detection mechanisms by modeling their outputs using subjective logic. We demonstrate the feasibility of our framework by using a combination of existing detection mechanisms to increase their misbehavior detection results. Information dissemination is one of the most-discussed applications for vehicular ad hoc networks (VANETs) and other ad hoc networks. To provide dependability for applications, information dissemination must be resilient against different kinds of attacks. Especially insider attackers, which may create valid messages that cannot easily be detected using cryptographic signatures alone, pose a viable threat to information dependability. Many proposals in existing work offer solutions to detect individual attack patterns using data consistency checks and other means. We propose a generic framework that can integrate a wide range of existing detection mechanisms, allows to combine their outputs to improve attack detection, and enables mechanism adaptation based on current attack likelihood. We employ subjective logic opinions, which enable flexible security mechanism output representation, and which we extend to support continuing operation in dynamic networks, such as VANETs. Simulation results show that our framework improves detection accuracy compared to applying individual mechanisms. We first describe a metric for uncertain probabilities called opinion, and subsequently a set of logical operators that can be used for logical reasoning with uncertain propositions. This framework which is called subjective logic uses elements from the Dempster-Shafer belief theory and we show that it is compatible with binary logic and probability calculus. We argue that the traditional notion of trust as a relation among entities, while useful, becomes insufficient for emerging data-centric mobile ad hoc networks. In these systems, setting the data trust level equal to the trust level of the data- providing entity would ignore system salient features, rendering applications ineffective and systems inflexible. This would be even more so if their operation is ephemeral, i.e., characterized by short-lived associations in volatile environments. In this paper, we address this challenge by extending the traditional notion of trust to data-centric trust: trustworthiness attributed to node-reported data per se. We propose a framework for data-centric trust establishment: First, trust in each individual piece of data is computed; then multiple, related but possibly contradictory, data are combined; finally, their validity is inferred by a decision component based on one of several evidence evaluation techniques. We consider and evaluate an instantiation of our framework in vehicular networks as a case study. Our simulation results show that our scheme is highly resilient to attackers and converges stably to the correct decision.