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An Intelligent Edge-Chain Enabled Access Control Mechanism For This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JIOT.2021.3061467, IEEE Internet of Things Journal JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 An Intelligent Edge-Chain Enabled Access Control Mechanism for IoV Yuanni Liu, Man Xiao, Shanzhi Chen, Fellow, IEEE, Fan Bai, Jianli Pan, Member, IEEE, Di Zhang, Senior Member, IEEE Abstract—The current security method of Internet of Vehicles the amounts of vehicles exceed one billion and is expected (IoV) systems is rare, which makes it vulnerable to various to reach two billion by 2035 [6], [7]. As explosive on-road attacks. The malicious and unauthorized nodes can easily in- devices are connecting to Internet, communication security vade the IoV systems to destroy the integrity, availability, and confidentiality of information resources shared among vehicles. becomes a big issue. As we know, the communication security Indeed, access control mechanism can remedy this. However, as method of current IoV devices is rare, making it easy to be a static method, it cannot timely response to these attacks. To attacked. Such malicious attacks include replay, camouflage, solve this problem, we propose an intelligent edge-chain enabled and message tampering attacks et al. [8]. The attacked IoV access control framework with vehicle nodes and RoadSide Units devices may rise potential risks, such as critical electronic (RSUs) in this study. In our scenario, vehicle nodes act as lightweight nodes, whereas RUSs serve as full and edge nodes control unit hacking and control, untrustworthy messages from to provide access control services. Considering the low accuracy attacked devices. Meanwhile, due to the pervasive distribution of risk prediction due to limited training sets, we leverage a and mobility features of vehicles, the current centralized cloud Generative Adversarial Networks (GANs) to convert the risk model is difficult to expand through massive weak devices [9]. prediction to a sequence generation. Moreover, aiming at the Moreover, the communication distance between IoV devices problems of gradient disappearance and mode collapse existed in the original GANs, we devise a Wasserstein Combined GANs and clouds is relatively long, which may consume amounts of (WCGAN). Simulation results demonstrate that WCGAN has bandwidth, time and energy. The cloud server is a bottleneck higher prediction accuracy than the original GANs. Additionally, of current IoV networks. A single failure of the server will it can also improve the accuracy of access control of Risk disrupt the entire network. Therefore, it is critical to prevent Prediction Based Access Control (RPBAC) model. unauthorized access to the IoV ecosystems on a large-scale Index Terms—Access Control, Blockchain, Edge Computing, area, which is one of the motivations of this paper. Generative Adversarial Networks, Internet of Vehicles. On the other hand, in our previous work, we have integrated blockchain and smart contracts capabilities into the intelli- I. INTRODUCTION gent Edge Computing (EC) framework as the foundations NTERNET of Vehicles (IoV) utilizes effective wireless [10], [11]. The goal is to develop a resource-oriented and I communication to share information among vehicles [1]– blockchain-embedded edge Internet of Things (IoT) frame- [3], which helps to achieve self-driving and maintain the work named Edge-Chain to provide the quality of experience traffic safety [4], [5]. Recently, a green-car report finds that services for IoT devices and to control their behaviors. In our intelligent Edge-Chain ecosystems, EC provides a possible Corresponding author: Di Zhang (E-mail: [email protected]). solution by pushing more resources to the edge, including This work is supported by the Open Fund of the Ministry of Education Key Laboratory of Networks Security and Intelligent Technology, Beijing intelligence, networking, computing, and storage resources. University of Posts and Telecommunications; the General Program of Natural The IoT applications that are delay-sensitive, data-sensitive, Science Foundation of Chongqing: cstc2020jcyj-msxmX1021; the Science and and bandwidth-constrained can benefit from it. In addition, for Technology Research Program of Chongqing Municipal Education Commis- sion: KJZD-K202000602; the Henan Provincial Key Research, Development the sake of resource and jurisdiction constraints, edge nodes and Promotion Project under grant: 551; the Henan Provincial Key Scientific cannot provide comprehensive services for IoT devices [12]. Research Project for College and University under grant: 21A510011. Therefore, blockchain can bring in a decentralized edge cloud Yuanni Liu is with the School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing 400065, solution, in which applications perform operations without a China (E-mail: [email protected]). trusted intermediary [13]. Additionally, combined with smart Man Xiao is with the School of Communication and Information Engi- contracts, blockchain can enable a trustless environment and neering, Chongqing university of Posts and Telecommunications, Chongqing 400065, China (E-mail: [email protected]). provide a system with validity, traceability, faulty tolerance, Shanzhi Chen is with the State Key Laboratory of Wireless Mobile and the automatic execution of policy. The integration of Communications, China Academy of Telecommunication Technology, Beijing blockchain also enables behavior identification of IoT devices, 100191, China (E-mail: [email protected]). Fan Bai is with the Beijing Institute of Spacecraft System Engineering, and fault tolerance through consensus mechanisms such as BeiJing 100094, China (E-mail: baifan [email protected]). practical byzantine fault tolerant [14], even when some edge Jianli Pan is with the Department of Computer Science, University of servers are compromised. Despite the proposed Edge-Chain Missouri, St. Louis, MO, 63121, USA (E-mail: [email protected]). Di Zhang is with the School of Information Engineering, Zhengzhou implemented in the IoT area, to the best of our knowledge, University, Zhengzhou 450001, China (E-mail: [email protected]). very few effort has combined the blockchain and smart con- Copyright (c) 20xx IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to [email protected]. 2327-4662 (c) 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. Authorized licensed use limited to: University of Missouri-St Louis. Downloaded on March 17,2021 at 23:04:51 UTC from IEEE Xplore. Restrictions apply. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JIOT.2021.3061467, IEEE Internet of Things Journal JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 2 tracts based on the edge IoV systems. Therefore, we try to activities are recorded as transactions in the blockchain leverage our Edge-Chain concept in the IoV access control for secure data auditing. An intelligent management and area. The goal is to construct an intelligent and blockchain- control module is built to provide the edge nodes with embedded Edge-Chain enabled system to tackle the key chal- intelligence to generate access control policies based lenges collectively by facilitating the secure access control for on the behavior data of vehicle nodes stored in the IoV devices. blockchain. The access rules and policies enforcement However, traditional access control mechanisms, such as are programmed as smart contracts and integrated into Role-Based Access Control (RBAC) [15] and Attribute-Based the proposed framework to regulate and audit the access Access Control (ABAC) [16], are usually static, which makes behaviors of vehicle nodes. it difficult to adapt to frequent changes of IoV devices. For • In the intelligent management and control module, we example, RBAC restricts network access based on the roles of propose a RPBAC model to predict the risk level based IoV devices. Once an IoV device has obtained the accessible on historical behaviors, and to assign IoV devices with permissions, the device will maintain the permissions for a different access authorities based on their predicted level. certain period. It is thus hard for the current IoV access control Aiming at the constraint of training set number on the ac- mechanism to make a timely response while encountering curacy of machine learning models, we select the GANs some attacks. To this end, machine learning algorithms have to convert the problem of risk prediction to a sequence been introduced to dynamic access control mechanisms in generation problem. The generator of GANs can generate the IoV systems. By analyzing the historical behavior of new sequences by fitting the probability distribution of IoV devices, a risk prediction model was constructed [17]. real data sets, which can be used to improve the accuracy However, machine learning-based accurate risk prediction of prediction of generator. requires a lot of data for training, which is tricky to be • In order to overcome the problems of gradient dis- adopted
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