Content uploaded by Virginia N. L. Franqueira
Author content
All content in this area was uploaded by Virginia N. L. Franqueira on May 24, 2018
Content may be subject to copyright.
A preview of the PDF is not available
TEAM: A Trust Evaluation And Management Framework in Context-enabled Vehicular Ad-hoc Networks
Abstract
Vehicular ad-hoc network (VANET) provides a unique platform for vehicles to intelligently
exchange critical information, such as collision avoidance messages. It is, therefore, paramount that this information remains reliable and authentic, i.e., originated from a legitimate and trusted vehicle. Trust establishment among vehicles can ensure security of a VANET by identifying dishonest vehicles and revoking messages with malicious content. For this purpose, several trust models (TMs) have been proposed but, currently, there is no effective way to compare how they would behave in practice under adversary conditions. To this end, we propose a novel trust evaluation and management (TEAM) framework, which serves as a unique paradigm for the design, management, and evaluation of TMs in various contexts and in presence of malicious vehicles. Our framework incorporates an asset-based threat model and ISO-based risk assessment for the identification of attacks against critical risks. The TEAM has been built using VEINS, an open source simulation environment which incorporates SUMO traffic simulator and OMNET++ discrete event simulator. The framework created has been tested with the implementation of three types of TMs (data oriented, entity oriented, and hybrid) under four different contexts of VANET based on the mobility of both honest and malicious vehicles. Results indicate that the TEAM is effective to simulate a wide range of TMs, where the efficiency is evaluated against different quality of service and security-related criteria. Such framework may be instrumental for planning smart cities and for car manufacturers.
... A holistic overview of the existing trust management mechanisms reveals that a number of trust-based parameters have been applied in different settings in order to measure and evaluate trust. The trust-based parameters include, but are not limited to, resource availability [6], similarity [19,25,26], familiarity [6,7,22,27], timeliness [7], context [19,20,28,29], cooperativeness [19,30], community-of-interest (CoI) [19,30], confidence [31,32], reward [28,33], attitude, subjective norms, and perceptual behavioral control [5], freshness of data [34], and packet delivery ratio [7,25,31,35,36]. Also, the selection of a trust-based threshold for determining trustworthy and untrustworthy behavior is crucial. ...
... A holistic overview of the existing trust management mechanisms reveals that a number of trust-based parameters have been applied in different settings in order to measure and evaluate trust. The trust-based parameters include, but are not limited to, resource availability [6], similarity [19,25,26], familiarity [6,7,22,27], timeliness [7], context [19,20,28,29], cooperativeness [19,30], community-of-interest (CoI) [19,30], confidence [31,32], reward [28,33], attitude, subjective norms, and perceptual behavioral control [5], freshness of data [34], and packet delivery ratio [7,25,31,35,36]. Also, the selection of a trust-based threshold for determining trustworthy and untrustworthy behavior is crucial. ...
... There are two important steps in trust management, i.e., building a trust model and evaluating a trust model [31]. The purpose of trust evaluation is to evaluate the accuracy, reliability, and practicality of an envisaged trust model. ...
The emerging yet promising paradigm of the Internet of Vehicles (IoV) has recently gained considerable attention from researchers from academia and industry. As an indispensable constituent of the futuristic smart cities, the underlying essence of the IoV is to facilitate vehicles to exchange safety-critical information with the other vehicles in their neighborhood, vulnerable pedestrians, supporting infrastructure, and the backbone network via vehicle-to-everything communication in a bid to enhance the road safety by mitigating the unwarranted road accidents via ensuring safer navigation together with guaranteeing the intelligent traffic flows. This requires that the safety-critical messages exchanged within an IoV network and the vehicles that disseminate the same are highly reliable (i.e., trustworthy); otherwise, the entire IoV network could be jeopardized. A state-of-the-art trust-based mechanism is, therefore, highly imperative for identifying and removing malicious vehicles from an IoV network. Accordingly, in this paper, a machine learning-based trust management mechanism, MESMERIC, has been proposed that takes into account the notions of direct trust (encompassing the trust attributes of interaction success rate, similarity, familiarity, and reward and punishment), indirect trust (involving confidence of a particular trustor on the neighboring nodes of a trustee, and the direct trust between the said neighboring nodes and the trustee), and context (comprising vehicle types and operating scenarios) in order to not only ascertain the trust of vehicles in an IoV network but to segregate the trustworthy vehicles from the untrustworthy ones by means of an optimal decision boundary. A comprehensive evaluation of the envisaged trust management mechanism has been carried out which demonstrates that it outperforms other state-of-the-art trust management mechanisms.
... Additionally, several research studies [26,27] analyze countermeasures in particular contexts. In [26], a method is suggested for determining how reliable information sent between dispersed automobiles in a protected automobile ad-hoc network (VANET) environment is. ...
... Additionally, several research studies [26,27] analyze countermeasures in particular contexts. In [26], a method is suggested for determining how reliable information sent between dispersed automobiles in a protected automobile ad-hoc network (VANET) environment is. For manufacturing equipment, a methodology for danger rating is put forth in [27], in which threats are anticipated depending on certain transmission schemes to determine the likelihood of network nodes becoming bargaining partners. ...
As a result of the emergence of new business paradigms and the development of the digital economy, the interaction between operations, services, things, and software across numerous fields and communities may now be processed through value chain networks. Despite the integration of all data networks, computing models, and distributed software that provides a broader cloud computing solution, the security solution is missing or inadequate, and more work is required to strengthen security requirements such as mutual entity trustworthiness, access controls, identity management, and data protection, all aspects of detecting and preventing attacks or threats. In order to combat cybersecurity threats, various international organizations, academic universities, institutions, and organizations have been working hard to establish cybersecurity frameworks (CSFs). This paper describes CSFs from the perspectives of standard organizations such as ISO CSF and NIST CSF, as well as several proposed frameworks from researchers, and briefly discusses their characteristics and features. The common ideas described in this study could be helpful for creating a CSF model in general.
The state-of-the-art framework for VANETs, Vehicles in Network Simulation (VEINS), is primarily sparse and fragmented. The combination of VANETs and VEINS can improve road safety, efficiency, and user experience for connected and autonomous vehicles. This research examined existing trends and knowledge gaps to provide actionable insights for technical contexts and researchers. Therefore, this systematic literature evaluation was conducted to create a full classification of the article ecosystem. The literature applies the VEINS framework to simulate and evaluate in-vehicle personalized entertainment recommendations based on real-time traffic data and user preferences. We examine service metrics for VANET-integrated vehicle content exchange. Three databases were consulted throughout this study: Scopus, ScienceDirect, and IEEE Xplore. The databases had extensive VANET-related research built on the VEINS framework. Then, screening was completed based on the services considerations. The topic is thoroughly covered in this categorization. Taxonomy proposes categories and subcategories. The initial group includes papers discussing different aspects of VANET-based VEINS framework applications (35/9878 total). The second group consists of pieces that focus on the answer (15/98 total). Network-related articles (48/98 total) make up the final section. This work concludes with a discussion of the VEINS framework’s design and bidirectional connectivity. This study could be helpful for researchers working on VANETs and the VEINS framework by highlighting areas where further development is necessary.
The integration of technologies like the Internet of Things (IoT), Big data, and Artificial Intelligence (AI) has empowered modern vehicles with the ability to communicate with one another for better cooperation on the roads. However, the communication between vehicles exposes the whole intelligent transportation system to new attack vectors. Malicious vehicles can spread misleading information, which, if acted upon, might result in traffic congestion, accidents, chaos, and even fatalities. As a countermeasure, the European Telecommunications Standards Institute (ETSI) proposes a framework, TR 103 460, for reporting abnormal behavior. However, there are many shortcomings, such as the absence of a peer-to-peer (P2P) misbehavior reporting (MR) service and the inability to secure the reporter's identity and reported information. To protect vehicles from abuse, we propose a P2P non-interactive zero-knowledge proof-based privacy-preserving MR framework. Upon testing, we found that the proposed framework prevented the disclosure of the reporter's identity and information and reduced the ITS-Stations' (ITS-Ss) exposure to misbehavior by 67.7% and 79.2% in suburban and highway traffic scenarios, respectively.
This paper aims to analyze the intellectual structure and research fronts in application information security in smart cities to identify research boundaries, trends, and new opportunities in the area. It applies bibliometric analyses to identify the main authors and their influences on information security and the smart city area. Moreover, this analysis focuses on journals indexed in Scopus databases. The results indicate that there is an opportunity for further advances in the adoption of information security policies in government institutions. Moreover, the production indicators presented herein are useful for the planning and implementation of information security policies and the knowledge of the scientific community about smart cities. The bibliometric analysis provides support for the visualization of the leading research technical collaboration networks among authors, co-authors, countries, and research areas. The methodology offers a broader view of the application information security in smart city areas and makes it possible to assist new research that may contribute to further advances. The smart cities topic has been receiving much attention in recent years, but to the best of our knowledge, there is no research on reporting new possibilities for advances. Therefore, this article may contribute to an emerging body of literature that explores the nature of application information security and smart cities research productivity to assist researchers in better understanding the current emerging of the area.
Trust establishment in Vehicular Ad hoc Networks (VANETs) is a challenging task due essentially to the high speed of vehicles, the long distances and the network topology dynamics. Furthermore, applications context evolves quickly at the same time the lifetime validity of data messages is short. In this paper, we set up a new distributed trust computing framework tailored to VANETs characteristics and aiming to solve the aforementioned challenges. The proposed framework is based on the investigation of the direct experience between neighboring vehicles without using any recommendation system. We also propose a tier-based messages dissemination technique in order to efficiently detect eavesdropped messages and fake events. Each vehicle checks the authenticity of the received data messages, and maintains a trust value for each of its neighbors. We analytically model the trust metrics evolution of malicious vehicles. Extensive simulations are conducted to show the validity of the proposed model and evaluate the efficiency of the proposed trust computing framework.
Dedicated Short Range Communication (DSRC) is proposed for Vehicle to Vehicle (V2V) communications to learn about significant events in the network from neighboring vehicles. However, these neighbors may be malicious and report incorrect events in order to take advantage of the system. The malicious nodes may also provide incorrect recommendations about their peers in order to exert a stronger influence on the receiver’s decision. Incorrect information and malicious nodes render the system unreliable for safety and emergency applications. In order to correctly identify the events as well as malicious nodes, a novel trust framework is proposed in this paper which studies all aspects of the trust in Connected Vehicle (CV) to CV communications. The nodes iteratively learn about the environment from received messages and then update the trust values of their neighbors. Nodes are classified on the basis of their trust values and reported events are also classified as true and false. Nodes advertise their recommendation about trusted and malicious neighbors. The proposed framework allows nodes to identify and filter recommendations from malicious nodes, and to discern true events. The performance of the proposed framework is evaluated experimentally using false and true positive rates, event detection probability and trust computation error. The proposed framework identifies malicious nodes and true events with high probability of more than 0.92 while keeping the trust computation error below 0.03.
The Internet of Things is a novel cutting edge technology that proffers to connect a plethora of digital devices endowed with several sensing, actuation, and computing capabilities with the Internet, thus offering manifold new services in the context of a smart city. The appealing IoT services and big data analytics are enabling smart city initiatives all over the world. These services are transforming cities by improving infrastructure and transportation systems, reducing traffic congestion, providing waste management, and improving the quality of human life. In this article, we devise a taxonomy to best bring forth a generic overview of the IoT paradigm for smart cities, integrated ICT, network types, possible opportunities and major requirements. Moreover, an overview of the up-to-date efforts from standard bodies is presented. Later, we give an overview of existing open source IoT platforms for realizing smart city applications followed by several exemplary case studies. In addition, we summarize the latest synergies and initiatives worldwide taken to promote IoT in the context of smart cities. Finally, we highlight several challenges in order to give future research directions.
The concept of trust in vehicular ad hoc networks (VANETs) is usually utilized to assess the trustworthiness of the received data as well as that of the sending entities. The quality of safety applications in VANETs largely depends on the trustworthiness of exchanged data. In this paper, we propose a self-organized distributed trust computing framework (DTCF) for VANETs to compute the trustworthiness of each vehicle, in order to filter out malicious nodes and recognize fully trusted nodes. The proposed framework is solely based on the investigation of the direct experience among vehicles without using any recommendation system. A tier-based dissemination technique for data messages is used to filter out non authentic messages and corresponding events before even going farther away from the source of the event. Extensive simulations are conducted using Omnet++/Sumo in order to investigate the efficiency of our framework and the consistency of the computed trust metrics in both urban and highway environments. Despite the high dynamics in such networks, our proposed DTCF is capable of detecting more than 85% of fully trusted vehicles, and filtering out virtually all malicious entities. The resulting average delay to detect malicious vehicles and fraudulent data is showed to be less than 1 second, and the computed trust metrics are shown to be highly consistent throughout the network.
The mobile industry’s evolution from 4G to 5G will lead to a deep progress on mobile applications that are widely used in some new environments, such as vehicular social networks (VSNs). In VSNs, which are considered the first automobile social networks, vehicular communication can facilitate large-scale data sharing between drivers and their neighbours. However, malicious users of VSNs can also disseminate false information over the network. Traditional public key infrastructure (PKI) cannot recognize these malicious users, as they all have authorized identities. Thus, a trust management mechanism is introduced to secure vehicular social data. This paper demonstrates a high-level trust management model and its deployment scheme based on a vehicular cloud system. We propose a layered trust management mechanism that benefits from efficient use of physical resources (e.g., computing, storage, communication cost) and explore its deployment in a VSN scenario based on a three-layer cloud computing architecture. Moreover, performance modeling of the proposed trust management scheme is conducted through a novel formal compositional approach – Performance Evaluation Process Algebra (PEPA). PEPA has superior features in compositionality and parsimony, which means that it can efficiently model systems with layered architectures and complex behaviours. PEPA also supports various numerical analyses through calculating its underlying continuous time Markov chains (CTMCs) directly or solving a set of approximated ordinary differential equations (ODEs). According to analysis outcomes, we analyzed several key performance properties of the scheme and related capacity issues in deployment. The findings also reveal an efficient investigation approach for evaluating the performances of trust models.
We argue that the traditional notion of trust as a relation among entities, while useful, becomes insufficient in ephemeral ad hoc networks. In this paper, we address the challenge of extending the traditional notion of trust to data-centric trust, that is, 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 the Dempster-Shafer Theory. We are especially interested in, yet not restricted to, ephemeral ad hoc networks, i.e., highly volatile systems with short-lived node encounters. 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 fast to the correct decision.