A hierarchical blockchain-enabled security-threat assessment architecture for IoV

Abstract

In Internet of Vehicles (IoV), the security-threat information of various traffic elements can be exploited by hackers to attack vehicles, resulting in accidents, privacy leakage. Consequently, it is necessary to establish security-threat assessment architectures to evaluate risks of traffic elements by managing and sharing security-threat information. Unfortunately, most assessment architectures process data in a centralized manner, causing delays in query services. To address this issue, in this paper, a Hierarchical Blockchain-enabled Security threat Assessment Architecture (HBSAA) is proposed, utilizing edge chains and global chains to share data. In addition, data virtualization technology is introduced to manage multi-source heterogeneous data, and a metadata association model based on attribute graph is designed to deal with complex data relationships. In order to provide high-speed query service, the ant colony optimization of key nodes is designed, and the HBSAA prototype is also developed and the performance is tested. Experimental results on the large-scale vulnerabilities data gathered from NVD demonstrate that the HBSAA not only shields data heterogeneity, but also reduces service response time.

Keywords

Internet of vehicles / Blockchain / Edge computing / Data virtualization / Data service composition

References

[1] J. Contreras-Castillo, S. Zeadally, J.A. Guerrero-Iba˜nez, Internet of vehicles: architecture, protocols and security, IEEE Internet Things J. 5 (5) (2017) 3701-3709.

[2] S. Malik, W. Sun, Analysis and simulation of cyber attacks against connected and autonomous vehicles, in: 2020 International Conference on Connected and Autonomous Driving (MetroCAD), IEEE, 2020, pp. 62-70.

[3] A. D. Kumar, K. N. R. Chebrolu, S. Kp, et al., A brief survey on autonomous vehicle possible attacks, exploits and vulnerabilities, arXiv preprint arXiv:1810.04144. htt ps://arxiv.org/abs/1810.04144.

[4] K. B. Kelarestaghi, M. Foruhandeh, K. Heaslip, R. Gerdes,Vehicle security: risk assessment in transportation, arXiv preprint arXiv:1804.07381. https://arxiv.org/abs/1804.07381.

[5] H.-K. Kong, M.K. Hong, T.-S. Kim, Security risk assessment framework for smart car using the attack tree analysis, J. Ambient Intell. Hum. Comput. 9 (3) (2018) 531-551.

[6] S.K. Dwivedi, R. Amin, S. Vollala, R. Chaudhry, Blockchain-based secured event-information sharing protocol in internet of vehicles for smart cities, Comput. Electr. Eng. 86 (2020) 106719.

[7] K. Qu, L. Meng, Y. Yang, A dynamic replica strategy based on markov model for hadoop distributed file system (hdfs), in: 2016 4th International Conference on Cloud Computing and Intelligence Systems (CCIS), IEEE, 2016, pp. 337-342.

[8] L. Mendiboure, M.A. Chalouf, F. Krief, Survey on blockchain-based applications in internet of vehicles, Comput. Electr. Eng. 84 (2020) 106646.

[9] K. Fan, Q. Pan, K. Zhang, Y. Bai, S. Sun, H. Li, Y. Yang, A secure and verifiable data sharing scheme based on blockchain in vehicular social networks, IEEE Trans. Veh. Technol. 69 (6) (2020) 5826-5835.

[10] A. Sadiq, N. Javaid, O. Samuel, A. Khalid, N. Haider, M. Imran, Efficient data trading and storage in internet of vehicles using consortium blockchain, in: 2020 International Wireless Communications and Mobile Computing (IWCMC), IEEE, 2020, pp. 2143-2148.

[11] X.-j. Liu, Y.-d. Yin, W. Chen, Y.-j. Xia, J.-l. Xu, L.-d. Han, Secure data sharing scheme in internet of vehicles based on blockchain, J. Zhejiang Univ. (Sci. Ed.) 55 (5) (2021) 957-965.

[12] Y. Liu, M. Xiao, S. Chen, F. Bai, J. Pan, D. Zhang, An intelligent edge-chain-enabled access control mechanism for iov, IEEE Internet Things J. 8 (15) (2021) 12231-12241.

[13] X. Liu, X. Yu, X. Ma, H. Kuang, A method to improve the fresh data query efficiency of blockchain, in: 2020 12th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), IEEE, 2020, pp. 823-827.

[14] O.V. Sawant, Combating dirty data using data virtualization, in: 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), IEEE, 2019, pp. 1-5.

[15] X. Luo, X. Gao, Z. Tan, J. Liu, X. Yang, G. Chen, D2-tree: a distributed double-layer namespace tree partition scheme for metadata management in large-scale storage systems, in: 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), IEEE, 2018, pp. 110-119.

[16] M.Y. Jung, J.W. Jang, Data management and searching system and method to provide increased security for iot platform, in: 2017 International Conference on Information and Communication Technology Convergence (ICTC), IEEE, 2017, pp. 873-878.

[17] T.G. Sarath, Centralized server based atm security system with statistical vulnerability prediction capability, in: 2017 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), IEEE, 2017, pp. 61-66.

[18] M. ur Rahman, V. Deep, S. Multhalli, Centralized vulnerability database for organization specific automated vulnerabilities discovery and supervision, in: 2016 International Conference on Research Advances in Integrated Navigation Systems (RAINS), IEEE, 2016, pp. 1-5.

[19] D. Zhang, Y. Liu, L. Dai, A.K. Bashir, A. Nallanathan, B. Shim, Performance analysis of fd-noma-based decentralized v2x systems, IEEE Trans. Commun. 67 (7) (2019) 5024-5036.

[20] M. Walkowski, M. Krakowiak, J. Oko, S. Sujecki, Distributed analysis tool for vulnerability prioritization in corporate networks, in: 2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), IEEE, 2020, pp. 1-6.

[21] R. Kothari, B. Jakheliya, V. Sawant, Implementation of a distributed p2p storage network, in: 2020 IEEE International Conference for Innovation in Technology (INOCON), IEEE, 2020, pp. 1-7.

[22] J. Wu, M. Dong, K. Ota, J. Li, Z. Guan, Big data analysis-based secure cluster management for optimized control plane in software-defined networks, IEEE Transactions on Network and Service Management 15 (1) (2018) 27-38.

[23] Z. Ma, J. Zhang, Y. Guo, Y. Liu, X. Liu, W. He, An efficient decentralized key management mechanism for vanet with blockchain, IEEE Trans. Veh. Technol. 69 (6) (2020) 5836-5849.

[24] Y. Yao, X. Chang, J. Mišić, V.B. Mišić, L. Li, Bla: blockchain-assisted lightweight anonymous authentication for distributed vehicular fog services, IEEE Internet Things J. 6 (2) (2019) 3775-3784.

[25] T. Jiang, H. Fang, H. Wang, Blockchain-based internet of vehicles: distributed network architecture and performance analysis, IEEE Internet Things J. 6 (3) (2018) 4640-4649.

[26] J. Pan, J. Wang, A. Hester, I. Alqerm, Y. Liu, Y. Zhao, Edgechain: an edge-iot framework and prototype based on blockchain and smart contracts, IEEE Internet Things J. 6 (3) (2018) 4719-4732.

[27] L. Cui, Z. Chen, S. Yang, Z. Ming, Q. Li, Y. Zhou, S. Chen, Q. Lu, A blockchain-based containerized edge computing platform for the internet of vehicles, IEEE Internet Things J. 8 (4) (2020) 2395-2408.

[28] P.K. Lahiri, D. Das, W. Mansoor, S. Banerjee, P. Chatterjee, A trustworthy blockchain based framework for impregnable iov in edge computing, in: 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), IEEE, 2020, pp. 26-31.

[29] A. Gusenkov, N. Bukharaev, E. Birialtsev, On ontology based data integration: problems and solutions, in: Journal of Physics: Conference Series, IOP Publishing, 2019 012059.

[30] A.K. Akanbi, M. Masinde, Semantic interoperability middleware architecture for heterogeneous environmental data sources, in: 2018 IST-Africa Week Conference (IST-Africa), IEEE, 2018, pp. 1-10.

[31] D. Alvarez-Coello, J.M. Gómez, Ontology-based integration of vehicle-related data, in: 2021 IEEE 15th International Conference on Semantic Computing (ICSC), IEEE, 2021, pp. 437-442.

[32] F. Ekaputra, M. Sabou, E. Serral Asensio, E. Kiesling, S. Biffl, Ontology-based data integration in multi-disciplinary engineering environments: a review, Open Journal of Information Systems 4 (1) (2017) 1-26.

[33] E. Martínez, D.M. Toma, S. Jirka, J. Del Río, Middleware for plug and play integration of heterogeneous sensor resources into the sensor web, Sensors 17 (12)(2017) 2923.

[34] A. Bogdanov, A. Degtyarev, N. Shchegoleva, V. Khvatov, V. Korkhov, Evolving principles of big data virtualization, in: International Conference on Computational Science and its Applications, Springer, 2020, pp. 67-81.

[35] A. Aleyasen, M.A. Soliman, L. Antova, F.M. Waas, M. Winslett, High-throughput adaptive data virtualization via context-aware query routing, in: 2018 IEEE International Conference on Big Data (Big Data), IEEE, 2018, pp. 1709-1718.

[36] T.A. Manoj Muniswamaiah, C. Tappert, Data virtualization for decision making in big data, Int. J. Sci. Eng. Appl. 10 (5) (2019) 45-53.

[37] M. Gottlieb, M. Shraideh, I. Fuhrmann, M. Böhm, H. Krcmar, Critical success factors for data virtualization: a literature review, The ISC International Journal of Information Security 11 (3) (2019) 131-137.

[38] Z. Zi-ye, L. Yu-long, H. Bei, Multi-source data integration method based on data virtualization technology, Comput. Mod. 11 (2019) 18-22.

[39] Y. Hua, X. Liu, Semantic-aware metadata organization for exact-matching queries, in: Searchable Storage in Cloud Computing, Springer, 2019, pp. 67-97.

[40] J. Fan, J. Yan, Y. Ma, L. Wang, Big data integration in remote sensing across a distributed metadata-based spatial infrastructure, Rem. Sens. 10 (1) (2017) 7.

[41] M. Khani Dehnoi, S. Araban, Automatic qos-aware web services composition based on set-cover problem, Int. J. Nonlinear Anal. Appl. 12 (1) (2021) 87-109.

[42] Y. Li, J. Hu, Z. Wu, C. Liu, F. Peng, Y. Zhang, Research on qos service composition based on coevolutionary genetic algorithm, Soft Comput. 22 (23) (2018) 7865-7874.

[43] H. Elmaghraoui, L. Benhlima, D. Chiadmi, Dynamic web service composition using and/or directed graph, in: 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), IEEE, 2017, pp. 1-8.

[44] C. Wang, X. Zhang, D. Chu, Research on service composition optimization method based on composite services qos, in: 2020 5th International Conference on Computational Intelligence and Applications (ICCIA), IEEE, 2020, pp. 206-210.

[45] Z. Wang, B. Cheng, W. Zhang, J. Chen, Q.- graphplan, Qos-aware automatic service composition with the extended planning graph, IEEE Access 8 (2020) 8314-8323.

[46] M.W. Wiśniewski,The Classification of Vulnerabilities of Iot Devices Based on Cve Database Contents, Ph.D. thesis, Instytut Informatyki, 2020.

[47] N. Mishra, R. Singh, S.K. Yadav, Analysis and vulnerability assessment of various models and frameworks in cloud computing, in: Advances in Data Sciences, Security and Applications, Springer, 2020, pp. 407-417.

[48] P.K. Singh, R. Singh, S.K. Nandi, K.Z. Ghafoor, D.B. Rawat, S. Nandi, Blockchain-based adaptive trust management in internet of vehicles using smart contract, IEEE Trans. Intell. Transport. Syst. 22 (6) (2020) 3616-3630.

[49] H. Liu, G. Jiang, L. Su, Y. Cao, F. Diao, L. Mi, Construction of power projects knowledge graph based on graph database neo4j, in: 2020 International Conference on Computer, Information and Telecommunication Systems (CITS), IEEE, 2020, pp. 1-4.

[50] M. Castro, B. Liskov, et al., Practical byzantine fault tolerance,in:Proceedings of the Third Symposium on Operating Systems Design and Implementation, USENIX Association, 1999, pp. 173-186.

[51] S. Kato, Y. Inagaki, M. Aoyama, A structural analysis method of oss development community evolution based on a semantic graph model, in: 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), IEEE, 2018, pp. 292-297.

[52] E. Neshati, A.A.P. Kazem, Qos-based cloud manufacturing service composition using ant colony optimization algorithm, Int. J. Adv. Comput. Sci. Appl. 9 (2018) 437-440.

[53] H. Zhang, D.-Y. Ye, W.-Z. Guo, A steiner point candidate-based heuristic framework for the steiner tree problem in graphs, J. Algorithm Comput. Technol. 10 (2) (2016) 99-114.

[54] R.-H. Li, L. Qin, J.X. Yu, R. Mao, Efficient and progressive group steiner tree search, in: Proceedings of the 2016 International Conference on Management of Data, ACM, 2016, pp. 91-106.

[55] B.L. Bullough, A.K. Yanchenko, C.L. Smith, J.R. Zipkin, Predicting exploitation of disclosed software vulnerabilities using open-source data, in: Proceedings of the 3rd ACM on International Workshop on Security and Privacy Analytics, ACM, 2017, pp. 45-53.

[56] N. Francis, A. Green, P. Guagliardo, L. Libkin, T. Lindaaker, V. Marsault, S. Plantikow, M. Rydberg, P. Selmer, A. Taylor, Cypher, An evolving query language for property graphs, in: Proceedings of the 2018 International Conference on Management of Data, ACM, 2018, pp. 1433-1445.