Open Access

ARTICLE

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 Alkhayyat^8,*

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:

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

Received 02 April 2022; Accepted 25 May 2022; Issue published 28 July 2022

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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