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Privacy Preserving Blockchain with Optimal Deep Learning Model for Smart Cities

K. Pradeep Mohan Kumar1, Jenifer Mahilraj2, D. Swathi3, R. Rajavarman4, Subhi R. M. Zeebaree5, Rizgar R. Zebari6, Zryan Najat Rashid7, Ahmed Alkhayyat8,*

1 Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India
2 Department of Computer Science and Information Technology, School of Engineering and Technology, Kebridehar University, Kebridehar, 250, Ethiopia
3 Department of Computer Science and Engineering, K.Ramakrishnan College of Engineering, Tiruchirappalli, 621112, India
4 Department of Computer Science and Engineering, K.Ramakrishnan College of Technology, Tiruchirappalli, 621112, India
5 Energy Department, Technical College of Engineering, Duhok Polytechnic University, Duhok, Iraq
6 Computer Science Department, College of Science, Nawroz University, Duhok, Iraq
7 Computer Network Department, Technical College of Informatics, Sulaimani Polytechnic University, Sulaimani, Iraq
8 College of Technical Engineering, The Islamic University, Najaf, Iraq

* Corresponding Author: Ahmed Alkhayyat. Email: email

Computers, Materials & Continua 2022, 73(3), 5299-5314. https://doi.org/10.32604/cmc.2022.030825

Abstract

Recently, smart cities have emerged as an effective approach to deliver high-quality services to the people through adaptive optimization of the available resources. Despite the advantages of smart cities, security remains a huge challenge to be overcome. Simultaneously, Intrusion Detection System (IDS) is the most proficient tool to accomplish security in this scenario. Besides, blockchain exhibits significance in promoting smart city designing, due to its effective characteristics like immutability, transparency, and decentralization. In order to address the security problems in smart cities, the current study designs a Privacy Preserving Secure Framework using Blockchain with Optimal Deep Learning (PPSF-BODL) model. The proposed PPSF-BODL model includes the collection of primary data using sensing tools. Besides, z-score normalization is also utilized to transform the actual data into useful format. Besides, Chameleon Swarm Optimization (CSO) with Attention Based Bidirectional Long Short Term Memory (ABiLSTM) model is employed for detection and classification of intrusions. CSO is employed for optimal hyperparameter tuning of ABiLSTM model. At the same time, Blockchain (BC) is utilized for secure transmission of the data to cloud server. This cloud server is a decentralized, distributed, and open digital ledger that is employed to store the transactions in different methods. A detailed experimentation of the proposed PPSF-BODL model was conducted on benchmark dataset and the outcomes established the supremacy of the proposed PPSF-BODL model over recent approaches with a maximum accuracy of 97.46%.

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Cite This Article

APA Style
Kumar, K.P.M., Mahilraj, J., Swathi, D., Rajavarman, R., Zeebaree, S.R.M. et al. (2022). Privacy preserving blockchain with optimal deep learning model for smart cities. Computers, Materials & Continua, 73(3), 5299-5314. https://doi.org/10.32604/cmc.2022.030825
Vancouver Style
Kumar KPM, Mahilraj J, Swathi D, Rajavarman R, Zeebaree SRM, Zebari RR, et al. Privacy preserving blockchain with optimal deep learning model for smart cities. Comput Mater Contin. 2022;73(3):5299-5314 https://doi.org/10.32604/cmc.2022.030825
IEEE Style
K.P.M. Kumar et al., "Privacy Preserving Blockchain with Optimal Deep Learning Model for Smart Cities," Comput. Mater. Contin., vol. 73, no. 3, pp. 5299-5314. 2022. https://doi.org/10.32604/cmc.2022.030825



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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