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Smart city article details
| Title | An Advance Encryption And Attack Detection Framework For Securing Smart Cities Data In Blockchain Using Deep Learning Approach |
|---|---|
| ID_Doc | 7407 |
| Authors | Kumar A.; Kumar S. |
| Year | 2024 |
| Published | Wireless Personal Communications, 135, 3 |
| DOI | http://dx.doi.org/10.1007/s11277-024-11050-1 |
| Abstract | Recently there has been a notable increase in interest in Internet of Things (IoT)-based smart cities from both business and academia. Smart cities can offer a range of smart apps, such as intelligent transportation, 4.0 industry, and intelligent banking, many others, to enhance the quality of life for inhabitants. Security is one of the main problems that smart cities face. Smart cities that use blockchain technology can offer higher levels of safety by storing transactions in an inviolable, safe, decentralized database. The cyberattacks happen increasingly frequently every day during transactions, it poses a security issue for smart cities. A novel attack detection and encryption model was created to overcome these issues and secure the data in private blockchain. After being gathered in raw form from a variety of IoT devices, the data had been processed using Savitzy Golay filter, min–max normalization, and missing values. Next, utilizing residual sum of squares, choose appropriate characteristics from the pre-processed data. The distributed network built on blockchain was given the chosen attributes. Trusted organizations were used to confirm the transaction, and their vital job was to validate the transaction. To validate the transaction, a modified Deep Neural Network (M-DNN) classifier was created. Once the transaction was successfully verified as normal, the information were secure in private blockchain using Elliptic Curve Integrated Encryption Scheme (ECIES); else the information was attack, the data was further move for attacks type detection process using hybrid LSTM-XGBoost classifier. The proposed model offer 96% for verification process, and 97% accuracy for attacks type detection process, also offer a low encryption time of 7.5 s. This indicates that the suggested approach performs better than alternative approaches that are presently in use. The suggested method’s result indicates that the transaction is carried out safely to create a secure atmosphere for smart cities. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. |
| Author Keywords | Blockchain, transaction verification; ECIES encryption; Hybrid LSTM-XGBoost; Modified DNN; Smart cities |
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