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Secured IoT architecture for personalized marketing using blockchain framework with deep learning technology

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Abstract

The Internet of Things (IoT), the convergence of blockchain and deep learning (DL) technologies presents exciting opportunities for innovation. The integration of a secured IoT architecture with a blockchain framework and DL technology offers a promising avenue for personalized marketing. The primary challenges in the realm of IoT-based personalized marketing are the vulnerability of user data and the need for a secure framework to protect sensitive information. The objective of the study is to develop a holistic solution by integrating blockchain technology and DL, offering a secure and personalized marketing architecture that safeguards user privacy while enhancing the effectiveness of targeted marketing strategies. Data collection, leveraging a blockchain network proves instrumental for expediting data transactions between the seed node and destination node while ensuring robust security measures. In this approach, a consortium blockchain integrates dispersed clusters of private to store encrypted data, increasing data transmission efficiency while ensuring operator privacy and security through off-chain storing and on-chain transmission synergy. The study then presented a lightweight hierarchical blockchain-based multi-chain code access control (AC) to safeguard the security and secrecy of IoT devices. Furthermore, federated DL is used to determine the best threshold and pertinent AC parameters, hence improving AC accuracy and privacy protection. Stacking involves training machine learning algorithms initially on training datasets and subsequently using these models to generate predictions for a new dataset. This new dataset, consisting of the predictions from the initial models, is then utilized as input for the ensemble algorithm. As a finding, the researchers presented an Ensemble Stacking approach combined with a deep long short-term memory-based intrusion detection method for detecting malicious or regular network traffic flow patterns in a cloud context. The proposed work is implemented using Python software. The findings show that the high accuracy value of 97.5% indicates the model’s proficiency in making precise and reliable predictions. The specificity value of 0.9045% indicates the maximum accuracy of the method. The accuracy reaches a remarkable 91.879%, outperforming the existing methods. The integration of a secured IoT architecture with a blockchain framework and DL technology offers a robust solution for personalized marketing while addressing challenges related to data refuge and privacy.

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  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, 500075, India

    Apurva Khandekar & Sheikh Fahad Ahmad

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  1. Apurva Khandekar
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  2. Sheikh Fahad Ahmad
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All authors contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript.

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Correspondence to Apurva Khandekar.

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Khandekar, A., Ahmad, S.F. Secured IoT architecture for personalized marketing using blockchain framework with deep learning technology. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04372-z

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