A proof-theoretic trust and reputation model for VANET

Vehicular Ad Hoc Networks (VANETs) are an important component of intelligent transportation systems, which are set to become part of global transportation infrastructure in the near future. In the context of such networks, security requirements need to rely on a combination of reputation of communicating agents and trust relations over the messaging framework. This is crucial in order to maintain dynamic and safe behaviour under all circumstances. Formal correctness, resolution of contradictions and proven safety of transitive operations in the presence of reputation and trust within the infrastructure remain mostly unexplored issues. This could lead to potentially disastrous situations, putting lives at risk. In this paper we provide a proof-theoretic interpretation of a reputation and trust model for VANET. This allows for formal verification through translation into the Coq proof assistant, and can guarantee consistency of messaging protocols and security of transitive transmissions

A proof-theoretic trust and reputation model for VANET

Vehicular Ad Hoc Networks (VANETs) are an important component of intelligent transportation systems, which are set to become part of global transportation infrastructure in the near future. In the context of such networks, security requirements need to rely on a combination of reputation of communicating agents and trust relations over the messaging framework. This is crucial in order to maintain dynamic and safe behaviour under all circumstances. Formal correctness, resolution of contradictions and proven safety of transitive operations in the presence of reputation and trust within the infrastructure remain mostly unexplored issues. This could lead to potentially disastrous situations, putting lives at risk. In this paper we provide a proof-theoretic interpretation of a reputation and trust model for VANET. This allows for formal verification through translation into the Coq proof assistant, and can guarantee consistency of messaging protocols and security of transitive transmissions

A Computational Model for Reputation and Ensemble-Based Learning Model for Prediction of Trustworthiness in Vehicular Ad Hoc Network

Vehicular ad hoc networks (VANETs) are a special kind of wireless communication network that facilitates vehicle-to-vehicle(V2V) and vehicle-to-infrastructure(V2I) communication. This technology exhibits the potential to enhance the safety of roads, efficiency of traffic, and comfort of passengers. However, this can lead to potential safety hazards and security risks, especially in autonomous vehicles that rely heavily on communication with other vehicles and infrastructure. Trust, the precision of data, and the reliability of data transmitted through the communication channel are the major problems in VANET. Cryptography-based solutions have been successful in ensuring the security of data transmission. However, there is still a need for further research to address the issue of fraudulent messages being sent from a legitimate sender. As a result, in this study, we have proposed a methodology for computing vehicles reputation and subsequently predicting the trustworthiness of vehicles in networks. The blockchain records the most recent assessment of the vehicle’s credibility. This will allow for greater transparency and trust in the vehicle’s history, as well as reduce the risk of fraud or tampering with the information. The trustworthiness of a vehicle is confirmed not just by the credibility, but also by its network behavior as observed during data transfer. To classify the trust, an ensemble learning model is used. In depth tests are run on the dataset to assess the effectiveness of the proposed ensemble learning with feature selection technique. The findings show that the proposed ensemble learning technique achieves a 99.98% accuracy rate, which is notably superior to the accuracy rates of the baseline models

A logic of negative trust

We present a logic to model the behaviour of an agent trusting or not trusting messages sent by another agent. The logic formalises trust as a consistency checking function with respect to currently available information. Negative trust is modelled in two forms: distrust, as the rejection of incoming inconsistent information; mistrust, as revision of previously held information becoming undesirable in view of new incoming inconsistent information, which the agent wishes to accept. We provide a natural deduction calculus, a relational semantics and prove soundness and completeness results. We overview a number of applications which have been investigated for the proof-theoretical formulation of the logic

Checking Trustworthiness of Probabilistic Computations in a Typed Natural Deduction System

In this paper we present the probabilistic typed natural deduction calculus TPTND, designed to reason about and derive trustworthiness properties of probabilistic computational processes, like those underlying current AI applications. Derivability in TPTND is interpreted as the process of extracting $n$ samples of possibly complex outputs with a certain frequency from a given categorical distribution. We formalize trust for such outputs as a form of hypothesis testing on the distance between such frequency and the intended probability. The main advantage of the calculus is to render such notion of trustworthiness checkable. We present a computational semantics for the terms over which we reason and then the semantics of TPTND, where logical operators as well as a Trust operator are defined through introduction and elimination rules. We illustrate structural and metatheoretical properties, with particular focus on the ability to establish under which term evolutions and logical rules applications the notion of trustworhtiness can be preserved