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.
Subjective logic @cite_6 is a framework for probabilistic information fusion, which is capable of representing not just a probabilistic truth value, but also a measure of uncertainty. It is similar to the more well-known Dempster-Shafer Theory, with the advantage that it integrates uncertainty directly, rather than adding it as a separate component. This has the advantage that fusion becomes easier. Subjective logic expresses the truth value of a statement through so-called opinions @math , which consist of a belief, disbelief, uncertainty and base rate. An intuitive interpretation is that belief is the probability that the statement is true, disbelief is the probability that it is false, and uncertainty represents the confidence in this evaluation. The base rate expresses the probability in the absence of evidence, which in this paper is assumed to be @math . An opinion can be converted into a prediction by computing the expectation, @math . Opinions are usually held by subjects about objects: for example, several detection mechanisms (subjects) can have different opinions about an object (a new packet). Subjective logic provides operators to fuse the opinions of these detectors with certain constraints.
@cite_6
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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. Probability theory and fuzzy logic have been presented as quite distinct theoretical foundations for reasoning and decision making in situations of uncertainty. This paper establishes a common basis for both forms of logic of uncertainty in which a basic uncertainty logic is defined in terms of a valuation on a lattice of propositions. The (non-truth-functional) connectives for conjunction, disjunction, equivalence, implication, and negation are defined in terms which closely resemble those of probability theory. Addition of the axiom of the excluded middle to the basic logic gives a standard probability logic. Alternatively, addition of a requirement for strong truth-functionality (truth-value of connective determined by truth-value of constituents) gives a fuzzy logic with connectives, including implication, as in Lukasiewicz' infinitely valued logic. A common semantics for all such variants is given in terms of binary responses from a population. The type of population, e.g., physical events, people, or neurons, determines whether the model is of physical probability, subjective belief, or human decision-making. The formal theory and the semantics together illustrate clearly the precise similarities and differences between fuzzy and probability logics. This thesis presents a logical formalism for representing and reasoning with probabilistic knowledge. The formalism differs from previous efforts in this area in a number of ways. Most previous work has investigated ways of assigning probabilities to the sentences of a logical language. Such an assignment fails to capture an important class of probabilistic assertions, empirical generalizations. Such generalizations are particularly important for AI, since they can be accumulated through experience with the world. Thus, they offer the possibility of reasoning in very general domains, domains where no experts are available to gather subjective probabilities from. A logic is developed which can represent these empirical generalizations. Reasoning can be performed through a proof theory which is shown to be sound and complete. Furthermore, the logic can represent and reason with a very general set of assertions, including many non-numeric assertions. This also is important for AI as numbers are usually not available. The logic makes it clear that there is an essential difference between empirical, or statistical, probabilities and probabilities assigned to sentences, e.g., subjective probabilities. The second part of the formalism is an inductive mechanism for assigning degrees of belief to sentences based on the empirical generalizations expressed in the logic. these degrees of belief have a strong advantage over subjective probabilities: they are founded on objective statistical knowledge about the world. Furthermore, the mechanism of assigning degrees of belief gives a natural answer to the question "Where do the probabilities come from:" they come from our experience with the world. The two parts of the formalism offer combined, interacting, but still clearly separated, plausible inductive inference and sound deductive inference. In real-world applications, knowledge bases consisting of all the available information for a specific domain, along with the current state of affairs, will typically contain contradictory data, coming from different sources, as well as data with varying degrees of uncertainty attached. An important aspect of the effort associated with maintaining such knowledge bases is deciding what information is no longer useful; pieces of information may be outdated; may come from sources that have recently been discovered to be of low quality; or abundant evidence may be available that contradicts them. In this paper, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing these basic issues. The formalism is capable of handling contradictory and uncertain data, and we study non-prioritized belief revision over probabilistic PreDeLP programs that can help with knowledge-base maintenance. For belief revision, we propose a set of rationality postulates -- based on well-known ones developed for classical knowledge bases -- that characterize how these belief revision operations should behave, and study classes of operators along with theoretical relationships with the proposed postulates, including representation theorems stating the equivalence between classes of operators and their associated postulates. We then demonstrate how our framework can be used to address the attribution problem in cyber security cyber warfare.