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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This paper introduces a framework for inference of timed temporal logic properties from data. The dataset is given as a finite set of pairs of finite-time system traces and labels, where the labels indicate whether the traces exhibit some desired behavior (e.g., a ship traveling along a safe route). We propose a decision-tree based approach for learning signal temporal logic classifiers. The method produces binary decision trees that represent the inferred formulae. Each node of the tree contains a test associated with the satisfaction of a simple formula, optimally tuned from a predefined finite set of primitives. Optimality is assessed using heuristic impurity measures, which capture how well the current primitive splits the data with respect to the traces' labels. We propose extensions of the usual impurity measures from machine learning literature to handle classification of system traces by leveraging upon the robustness degree concept. The proposed incremental construction procedure greatly improves the execution time and the accuracy compared to existing algorithms. We present two case studies that illustrate the usefulness and the computational advantages of the algorithms. The first is an anomaly detection problem in a maritime environment. The second is a fault detection problem in an automotive powertrain system. 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. In the field of infrastructures’ surveillance and protection, it is important to make decisions based on activities occurring in the environment and its local context and conditions. In this paper we use an active rule based event processing architecture in order to make sense of situations from the combination of different signals received by the rule engine. However obtaining some high level information automatically is not without risks, especially in sensitive environments, and detection mistakes can happen for various reasons: the signal’s source can be defective, whether it is human—miss-interpretation of the signal—or computed—material malfunction; the aggregation rules can be wrong syntaxically, for example when a rule will never be triggered or a situation never detected; the interpretation given to the combination of signals does not correspond to the reality on the field—because the knowledge of the rule designer is subjective or because the environment evolves over-time—the rules are therefore incorrect semantically. In this paper, a new approach is proposed to avoid the third kind of error sources. We present a hybrid machine learning technique adapted to the complexity of the rules’ representation, in order to create a system more conform to reality. The proposed approach uses a combination of an Association Rule Mining algorithm and Inductive Logic Programming for rule induction. Empirical studies on simulated datasets demonstrate how our method can contribute to sensible systems such as the security of a public or semi-public place. The rise of social network and crowdsourcing platforms makes it convenient to take advantage of the collective intelligence to estimate true labels of questions of interest. However, input from workers is often noisy and even malicious. Trust is used to model workers in order to better estimate true labels of questions. We observe that questions are often not independent in real life applications. Instead, there are logical relations between them. Similarly, workers that provide answers are not independent of each other either. Answers given by workers with similar attributes tend to be correlated. Therefore, we propose a novel unified graphical model consisting of two layers. The top layer encodes domain knowledge which allows users to express logical relations using first-order logic rules and the bottom layer encodes a traditional crowdsourcing graphical model. Our model can be seen as a generalized probabilistic soft logic framework that encodes both logical relations and probabilistic dependencies. To solve the collective inference problem efficiently, we have devised a scalable joint inference algorithm based on the alternating direction method of multipliers. Finally, we demonstrate that our model is superior to state-of-the-art by testing it on multiple real-world datasets.