The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
TP2SF: A Trustworthy Privacy-Preserving Secured Framework for sustainable smart cities by leveraging blockchain and machine learning
Abstract
Abstract With the advancement in sensor technology and the proliferation of low-cost electronic circuits, Internet of Things (IoT) is emerging as a promising technology for realization of smart cities. However, challenges such as security, privacy, trust, scalability, verifiability, and centralization prevent faster adaptations of IoT-driven smart cities. Thus, in this paper, a Trustworthy Privacy-Preserving Secured Framework (TP2SF) for smart cities is presented. This framework includes three modules namely: a trustworthiness module, a two-level privacy module, and an intrusion detection module. In trustworthiness module, address-based blockchain reputation system is designed. In the two-level privacy module, a blockchain based enhanced Proof of Work (ePoW) technique is simultaneously applied with Principal Component analysis (PCA) to transform data into a new reduced shape for preventing inference and poisoning attacks. In the intrusion detection module, an optimized gradient tree boosting system (XGBoost) is deployed. Finally, due to inherited strengths and weaknesses of Fog–Cloud architecture, we present a blockchain-IPFS integrated Fog–Cloud infrastructure namely, CloudBlock and FogBlock to deploy proposed TP2SF framework in smart city. The TP2SF framework is evaluated using two realistic IoT-based datasets, namely ToN-IoT and BoT-IoT. The findings indicate the superiority of TP2SF framework over other state-of-the-art techniques in both nonblockchain and blockchain systems.
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Citations
Cites
A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT
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Blockchain for smart communities: applications, challenges and opportunities
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Trust management in a blockchain based fog computing platform with trustless smart oracles
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Cited by
Kumar, P., Kumar, R., Srivastava, G., Gupta, G. P., Tripathi, R., Gadekallu, T. R., & Xiong, N. N. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3089435 · Cited by 360
Self-citationDeep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0
Ferrag, M. A., Shu, L., Djallel, H., & Choo, K.-K. R. (2021) · Electronics · doi:10.3390/electronics10111257 · Cited by 253
Kumar, P., Kumar, R., Kumar, A., Franklin, A. A., Garg, S., & Singh, S. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3191601 · Cited by 117
Self-citationRetractedBlockchain technology in the smart city: A bibliometric review
Rejeb, A., Rejeb, K., Simske, S. J., & Keogh, J. G. (2022) · Quality & Quantity · doi:10.1007/s11135-021-01251-2 · Cited by 104
A Survey of Machine and Deep Learning Methods for Privacy Protection in the Internet of Things
Rodríguez, E., Otero, B., & Canal, R. (2023) · Sensors · doi:10.3390/s23031252 · Cited by 82
Metadata
- Authors
- Kumar, P., Gupta, G. P., & Tripathi, R.
- Venue
- Journal of Systems Architecture
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 115
- Pages
- 101954
- ISSN
- 1383-7621 (ISSN-L)
- OpenAlex
- W3108615370
- Cited by
- 208
- References
- 7
- FWCI
- 27.66
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kumar, P., Gupta, G. P., & Tripathi, R. (2021). TP2SF: A Trustworthy Privacy-Preserving Secured Framework for sustainable smart cities by leveraging blockchain and machine learning. Journal of Systems Architecture. https://doi.org/10.1016/j.sysarc.2020.101954
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.