Privacy Preserving Blockchain with Optimal Deep Learning Model for Smart Cities

【Author】 Kumar, K. Pradeep Mohan; Mahilraj, Jenifer; Swathi, D.; Rajavarman, R.; Zeebaree, Subhi R. M.; Zebari, Rizgar R.; Rashid, Zryan Najat; Alkhayyat, Ahmed

【Source】CMC-COMPUTERS MATERIALS & CONTINUA

【影响因子】3.860

【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%.

【Keywords】Blockchain; smart city; security; intrusion detection system; chameleon swarm optimization; deep learning; parameter tuning

【发表时间】2022

【收录时间】2022-10-12

【文献类型】实验仿真

【主题类别】

区块链技术-协同技术-隐私计算