314 research outputs found
A Trust Management Framework for Vehicular Ad Hoc Networks
The inception of Vehicular Ad Hoc Networks (VANETs) provides an opportunity for road users and public infrastructure to share information that improves the operation of roads and the driver experience. However, such systems can be vulnerable to malicious external entities and legitimate users. Trust management is used to address attacks from legitimate users in accordance with a userās trust score. Trust models evaluate messages to assign rewards or punishments. This can be used to influence a driverās future behaviour or, in extremis, block the driver. With receiver-side schemes, various methods are used to evaluate trust including, reputation computation, neighbour recommendations, and storing historical information. However, they incur overhead and add a delay when deciding whether to accept or reject messages. In this thesis, we propose a novel Tamper-Proof Device (TPD) based trust framework for managing trust of multiple drivers at the sender side vehicle that updates trust, stores, and protects information from malicious tampering. The TPD also regulates, rewards, and punishes each specific driver, as required. Furthermore, the trust score determines the classes of message that a driver can access. Dissemination of feedback is only required when there is an attack (conflicting information). A Road-Side Unit (RSU) rules on a dispute, using either the sum of products of trust and feedback or official vehicle data if available. These āuntrue attacksā are resolved by an RSU using collaboration, and then providing a fixed amount of reward and punishment, as appropriate. Repeated attacks are addressed by incremental punishments and potentially driver access-blocking when conditions are met. The lack of sophistication in this fixed RSU assessment scheme is then addressed by a novel fuzzy logic-based RSU approach. This determines a fairer level of reward and punishment based on the severity of incident, driver past behaviour, and RSU confidence. The fuzzy RSU controller assesses judgements in such a way as to encourage drivers to improve their behaviour. Although any driver can lie in any situation, we believe that trustworthy drivers are more likely to remain so, and vice versa. We capture this behaviour in a Markov chain model for the sender and reporter driver behaviours where a driverās truthfulness is influenced by their trust score and trust state. For each trust state, the driverās likelihood of lying or honesty is set by a probability distribution which is different for each state. This framework is analysed in Veins using various classes of vehicles under different traffic conditions. Results confirm that the framework operates effectively in the presence of untrue and inconsistent attacks. The correct functioning is confirmed with the system appropriately classifying incidents when clarifier vehicles send truthful feedback. The framework is also evaluated against a centralized reputation scheme and the results demonstrate that it outperforms the reputation approach in terms of reduced communication overhead and shorter response time. Next, we perform a set of experiments to evaluate the performance of the fuzzy assessment in Veins. The fuzzy and fixed RSU assessment schemes are compared, and the results show that the fuzzy scheme provides better overall driver behaviour. The Markov chain driver behaviour model is also examined when changing the initial trust score of all drivers
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This ļ¬fth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ļ¬elds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiļ¬ed Proportional Conļ¬ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiļ¬ers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiļ¬cation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiļ¬cation.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiļ¬cation, and hybrid techniques mixing deep learning with belief functions as well
Incentivizing Private Data Sharing in Vehicular Networks: A Game-Theoretic Approach
In the context of evolving smart cities and autonomous transportation
systems, Vehicular Ad-hoc Networks (VANETs) and the Internet of Vehicles (IoV)
are growing in significance. Vehicles are becoming more than just a means of
transportation; they are collecting, processing, and transmitting massive
amounts of data to make driving safer and more convenient. However, this
advancement ushers in complex issues concerning the centralized structure of
traditional vehicular networks and the privacy and security concerns around
vehicular data. This paper offers a novel, game-theoretic network architecture
to address these challenges. Our approach decentralizes data collection through
distributed servers across the network, aggregating vehicular data into
spatio-temporal maps via secure multi-party computation (SMPC). This strategy
effectively reduces the chances of adversaries reconstructing a vehicle's
complete path, increasing privacy. We also introduce an economic model grounded
in game theory that incentivizes vehicle owners to participate in the network,
balancing the owners' privacy concerns with the monetary benefits of data
sharing. This model aims to maximize the data consumer's utility from the
gathered sensor data by determining the most suitable payment to participating
vehicles, the frequency in which these vehicles share their data, and the total
number of servers in the network. We explore the interdependencies among these
parameters and present our findings accordingly. To define meaningful utility
and loss functions for our study, we utilize a real dataset of vehicular
movement traces.Comment: To Appear in the Proceedings of The 2023 IEEE 98th Vehicular
Technology Conference (VTC2023-Fall), 6 pages, 5 figure
Blockchain-Coordinated Frameworks for Scalable and Secure Supply Chain Networks
Supply chains have progressed through time from being limited to a few regional traders to becoming complicated business networks. As a result, supply chain management systems now rely significantly on the digital revolution for the privacy and security of data. Due to key qualities of blockchain, such as transparency, immutability and decentralization, it has recently gained a lot of interest as a way to solve security, privacy and scalability problems in supply chains. However conventional blockchains are not appropriate for supply chain ecosystems because they are computationally costly, have a limited potential to scale and fail to provide trust. Consequently, due to limitations with a lack of trust and coordination, supply chains tend to fail to foster trust among the networkās participants. Assuring data privacy in a supply chain ecosystem is another challenge. If information is being shared with a large number of participants without establishing data privacy, access control risks arise in the network. Protecting data privacy is a concern when sending corporate data, including locations, manufacturing supplies and demand information. The third challenge in supply chain management is scalability, which continues to be a significant barrier to adoption. As the amount of transactions in a supply chain tends to increase along with the number of nodes in a network. So scalability is essential for blockchain adoption in supply chain networks. This thesis seeks to address the challenges of privacy, scalability and trust by providing frameworks for how to effectively combine blockchains with supply chains. This thesis makes four novel contributions. It first develops a blockchain-based framework with Attribute-Based Access Control (ABAC) model to assure data privacy by adopting a distributed framework to enable fine grained, dynamic access control management for supply chain management. To solve the data privacy challenge, AccessChain is developed. This proposed AccessChain model has two types of ledgers in the system: local and global. Local ledgers are used to store business contracts between stakeholders and the ABAC model management, whereas the global ledger is used to record transaction data. AccessChain can enable decentralized, fine-grained and dynamic access control management in SCM when combined with the ABAC model and blockchain technology (BCT). The framework enables a systematic approach that advantages the supply chain, and the experiments yield convincing results. Furthermore, the results of performance monitoring shows that AccessChainās response time with four local ledgers is acceptable, and therefore it provides significantly greater scalability. Next, a framework for reducing the bullwhip effect (BWE) in SCM is proposed. The framework also focuses on combining data visibility with trust. BWE is first observed in SC and then a blockchain architecture design is used to minimize it. Full sharing of demand data has been shown to help improve the robustness of overall performance in a multiechelon SC environment, especially for BWE mitigation and cumulative cost reduction. It is observed that when it comes to providing access to data, information sharing using a blockchain has some obvious benefits in a supply chain. Furthermore, when data sharing is distributed, parties in the supply chain will have fair access to other partiesā data, even though they are farther downstream. Sharing customer demand is important in a supply chain to enhance decision-making, reduce costs and promote the final end product. This work also explores the ability of BCT as a solution in a distributed ledger approach to create a trust-enhanced environment where trust is established so that stakeholders can share their information effectively. To provide visibility and coordination along with a blockchain consensus process, a new consensus algorithm, namely Reputation-based proof-of cooperation (RPoC), is proposed for blockchain-based SCM, which does not involve validators to solve any mathematical puzzle before storing a new block. The RPoC algorithm is an efficient and scalable consensus algorithm that selects the consensus node dynamically and permits a large number of nodes to participate in the consensus process. The algorithm decreases the workload on individual nodes while increasing consensus performance by allocating the transaction verification process to specific nodes. Through extensive theoretical analyses and experimentation, the suitability of the proposed algorithm is well grounded in terms of scalability and efficiency.
The thesis concludes with a blockchain-enabled framework that addresses the issue of preserving privacy and security for an open-bid auction system. This work implements a bid management system in a private BC environment to provide a secure bidding scheme. The novelty of this framework derives from an enhanced approach for integrating BC structures by replacing the original chain structure with a tree structure. Throughout the online world, user privacy is a primary concern, because the electronic environment enables the collection of personal data. Hence a suitable cryptographic protocol for an open-bid auction atop BC is proposed. Here the primary aim is to achieve security and privacy with greater efficiency, which largely depends on the effectiveness of the encryption algorithms used by BC. Essentially this work considers Elliptic Curve Cryptography (ECC) and a dynamic cryptographic accumulator encryption algorithm to enhance security between auctioneer and bidder. The proposed e-bidding scheme and the findings from this study should foster the further growth of BC strategies
Security and Privacy for Modern Wireless Communication Systems
The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in nodeāedgeācloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks
A Comprehensive Survey on the Cooperation of Fog Computing Paradigm-Based IoT Applications: Layered Architecture, Real-Time Security Issues, and Solutions
The Internet of Things (IoT) can enable seamless communication between millions of billions of objects. As IoT applications continue to grow, they face several challenges, including high latency, limited processing and storage capacity, and network failures. To address these stated challenges, the fog computing paradigm has been introduced, purpose is to integrate the cloud computing paradigm with IoT to bring the cloud resources closer to the IoT devices. Thus, it extends the computing, storage, and networking facilities toward the edge of the network. However, data processing and storage occur at the IoT devices themselves in the fog-based IoT network, eliminating the need to transmit the data to the cloud. Further, it also provides a faster response as compared to the cloud. Unfortunately, the characteristics of fog-based IoT networks arise traditional real-time security challenges, which may increase severe concern to the end-users. However, this paper aims to focus on fog-based IoT communication, targeting real-time security challenges. In this paper, we examine the layered architecture of fog-based IoT networks along working of IoT applications operating within the context of the fog computing paradigm. Moreover, we highlight real-time security challenges and explore several existing solutions proposed to tackle these challenges. In the end, we investigate the research challenges that need to be addressed and explore potential future research directions that should be followed by the research community.Ā©2023 The Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed
Performance Modeling of Vehicular Clouds Under Different Service Strategies
The amount of data being generated at the edge of the Internet is rapidly rising as a result of the Internet of Things (IoT). Vehicles themselves are contributing enormously to data generation with their advanced sensor systems. This data contains contextual information; it's temporal and needs to be processed in real-time to be of any value. Transferring this data to the cloud is not feasible due to high cost and latency. This has led to the introduction of edge computing for processing of data close to the source. However, edge servers may not have the computing capacity to process all the data. Future vehicles will have significant computing power, which may be underutilized, and they may have a stake in the processing of the data. This led to the introduction of a new computing paradigm called vehicular cloud (VC), which consists of interconnected vehicles that can share resources and communicate with each other. The VCs may process the data by themselves or in cooperation with edge servers.
Performance modeling of VCs is important, as it will help to determine whether it can provide adequate service to users. It will enable determining appropriate service strategies and the type of jobs that may be served by the VC such that Quality of service (QoS) requirements are met. Job completion time and throughput of VCs are important performance metrics. However, performance modeling of VCs is difficult because of the volatility of resources. As vehicles join and leave the VC, available resources vary in time. Performance evaluation results in the literature are lacking, and available results mostly pertain to stationary VCs formed from parked vehicles. This thesis proposes novel stochastic models for the performance evaluation of vehicular cloud systems that take into account resource volatility, composition of jobs from multiple tasks that can execute concurrently under different service strategies. First, we developed a stochastic model to analyze the job completion time in a VC system deployed on a highway with service interruption. Next, we developed a model to analyze the job completion time in a VC system with a service interruption avoidance strategy. This strategy aims to prevent disruptions in task service by only assigning tasks to vehicles that can complete the tasksā execution before they leave the VC. In addition to analyzing job completion time, we evaluated the computing capacity of VC systems with a service interruption avoidance strategy, determining the number of jobs a VC system can complete during its lifetime. Finally, we studied the computing capacity of a robotaxi fleet, analyzing the average number of tasks that a robotaxi fleet can serve to completion during a cycle. By developing these models, conducting various analyses, and comparing the numerical results of the analyses to extensive Monte Carlo simulation results, we gained insights into job completion time, computing capacity, and overall performance of VC systems deployed in different contexts
A framework to assess the authenticity of subjective information in the integration of blockchain technology - an application in supply chain management.
Blockchain technology has burgeoned due to the booming value of cryptocurrency capitalisation. It enables financial transactions to be carried out without a bank or a third party regulating them. Aspects such as privacy, trust, security, and transparency of a transaction are ensured by its immutability characteristics. These features of blockchain have resulted in it being used in other domains, such as supply chains.
As the adoption of blockchain has expanded, it is currently being applied in domains where there is an equal chance of opinions, facts, and personal commitment being part of the business operation. One such area is proactive supply chain risk management (SCRM). Previous researchers have often highlighted the fraudulent behaviour of supply chain partners who do not disclose information on the risks that impact their operations. Despite this, very few researchers consider subjective information in the processing of blockchain. Those who take this into consideration acknowledge the presence of such information but do not utilize it in the processing of blockchain. Blockchain can address this problem by encoding each partner's commitment to SCRM and achieving consensus. However, before this can be achieved, a key challenge to address is the inability of existing consensus mechanisms such as Proof of Work (PoW), Proof of Authority (PoA) and Proof of Stake (PoS) to deal with information that does not have a digital footprint such as claims, opinions, promises, or communications between supply chain partners when they form a Service Level Agreement (SLA). This type of information is called subjective information. Addressing this research gap is very important if the true potential of blockchain in providing a single source of truth in a domain, irrespective of what type of information is used, is to be achieved. Thus, future research should investigate a new consensus mechanism with a unified framework that not only stores this information but determines its trustworthiness.
This thesis addresses this gap by proposing the Proof of Earnestness (PoE) consensus mechanism which accounts for the authenticity, legitimacy and trustworthiness of information that does not have a digital footprint. This thesis also proposes the Subjective Information Authenticity Earnestness Framework (SIAEF) as the overarching framework that assists PoE in achieving its aim. SIAEF comprises four modules, namely the Identification module, the Mapping module, the Data collection & Impact determination module and Local consensus & Global legitimacy module. These modules provide a complete solution to identify subjective information in an SLA, detect the potential operational risk term which may potentially impact a responsible partner who commits to the subjective information, collate its real-world occurrences in the geographic region of interest, then determine the responsible partner's adherence to what it had recommitted. SIAEF assists in achieving PoE's aim of generating a digital footprint of a responsible partnerās earnestness in communicating subjective information. Once this footprint is generated, existing consensus mechanisms such as PoW, PoS or PoA are used to encode this information in blockchains. Each module is computed in the application of machine learning and natural language processing with recent techniques, metrics and evaluation. The applicability of SIAEF and PoE is tested in a real-world blockchain environment by deploying it as a Decentralized application (Dapp) and applying it in BscScan Testnet which is an official test blockchain network.
The thesis will contribute to the existing literature by proposing a new consensus mechanism and its framework to assist the existing blockchain framework in verifying and validating the truthfulness of subjective information. Supply chain partners can use the SIAEF framework as a reference to choose a potential partner with whom to form an SLA, preventing opportunistic and fraudulent behaviours in supply chain management
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Cyberattacks and security of cloud computing: a complete guideline
Cloud computing is an innovative technique that offers shared resources for stock cache and server management. Cloud computing saves time and monitoring costs for any organization and turns technological solutions for large-scale systems into server-to-service frameworks. However, just like any other technology, cloud computing opens up many forms of security threats and problems. In this work, we focus on discussing different cloud models and cloud services, respectively. Next, we discuss the security trends in the cloud models. Taking these security trends into account, we move to security problems, including data breaches, data confidentiality, data access controllability, authentication, inadequate diligence, phishing, key exposure, auditing, privacy preservability, and cloud-assisted IoT applications. We then propose security attacks and countermeasures specifically for the different cloud models based on the security trends and problems. In the end, we pinpoint some of the futuristic directions and implications relevant to the security of cloud models. The future directions will help researchers in academia and industry work toward cloud computing security
A Multireceiver Certificateless Signcryption (MCLS) Scheme
User authentication and message confidentiality are the basic security requirements of high-end applications such as multicast communication and distributed systems. Several efficient signature-then-encrypt cryptographic schemes have been proposed to offer these security requirements with lower computational cost and communication overhead. However, signature-then-encryption techniques take more computation time than signcryption techniques. Signcryption accomplishes both digital signature and public key encryption functions in a single logical step and at a much lower cost than ``signature followed by encryption.\u27\u27 Several signcryption schemes based on bilinear pairing operations have been proposed. Similarly, anonymous multi-receiver encryption has recently risen in prominence in multicast communication and distributed settings, where the same messages are sent to several receivers but the identity of each receiver should remain private. Anonymous multi-receiver encryption allows a receiver to obtain the plaintext by decrypting the ciphertext using their own private key, while their identity is kept secret to anyone, including other receivers. Among the Certificateless Multi-receiver Encryption (CLMRE) schemes that have been introduced, Hung et al. proposed an efficient Anonymous Multireceiver Certificateless Encryption (AMCLE) scheme ensuring confidentiality and anonymity based on bilinear pairings and is secure against IND-CCA and ANON-CCA.
In this paper, we substantially extend Hung et al.ās multireceiver certificateless encryption scheme to a Multireceiver Certificateless Signcryption (MCLS) scheme that provides confidentiality along with authentication. We show that, as compared to Hung et al.ās encryption scheme, our signcryption scheme requires only three additional multiplication operations for signcryption and unsigncryption phases. Whereas, the signcryption cost is linear with the number of designated receivers while the unsigncryption cost remains constant for each designated receiver. We compare the results with other existing single receiver and multireceiver signcryption schemes in terms of number of operations, exemption of key escrow problem, and public key settings. The scheme proposed in this paper is more efficient for single and multireceiver signcryption schemes while providing exemption from the key escrow problem, and working in certificateless public key settings
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